diff --git a/min_max_utils/HTTPLIB2Capture.py b/connection/HTTPLIB2Capture.py
similarity index 100%
rename from min_max_utils/HTTPLIB2Capture.py
rename to connection/HTTPLIB2Capture.py
diff --git a/connection/ModelPredictionsReceiver.py b/connection/ModelPredictionsReceiver.py
new file mode 100644
index 0000000..9981eb4
--- /dev/null
+++ b/connection/ModelPredictionsReceiver.py
@@ -0,0 +1,63 @@
+import requests
+import numpy as np
+from logging import Logger
+from PIL import Image
+import io
+
+
+PORT = 5000
+
+
+class ModelPredictionsReceiver:
+    def __init__(self, server_url: str, logger: Logger) -> None:
+        self._server_url = server_url
+        self._logger = logger
+
+    @staticmethod
+    def _convert_image2bytes(image: np.array, format='PNG') -> io.BytesIO:
+        pil_image = Image.fromarray(image)
+        img_byte_arr = io.BytesIO()
+        pil_image.save(img_byte_arr, format=format)
+        img_byte_arr.seek(0)
+        return img_byte_arr
+
+    def predict_human(self, img: np.array):
+        try:
+            response = requests.post(
+                f"{self._server_url}:{PORT}/predict_human",
+                files={
+                    "image": ("image", self._convert_image2bytes(img), "image/png")
+                }
+            )
+            response.raise_for_status()
+            return np.array(response.json().get("coordinates"))
+        except Exception as exc:
+            self._logger.critical("Cannot send request to model server. Error - {}".format(exc))
+    
+    def predict_bottles(self, img: np.array):
+        try:
+            response = requests.post(
+                f"{self._server_url}:{PORT}/predict_bottles",
+                files={
+                    "image": ("image", self._convert_image2bytes(img), "image/png")
+                }
+            )
+            response.raise_for_status()
+            return np.array(response.json().get("coordinates"))
+        except Exception as exc:
+            self._logger.critical("Cannot send request to model server. Error - {}".format(exc))
+
+    def predict_boxes(self, img: np.array):
+        try:
+            response = requests.post(
+                f"{self._server_url}:{PORT}/predict_boxes",
+                files={
+                    "image": ("image", self._convert_image2bytes(img), "image/png")
+                }
+            )
+            response.raise_for_status()
+            return np.array(response.json().get("coordinates"))
+        except Exception as exc:
+            self._logger.critical("Cannot send request to model server. Error - {}".format(exc))
+
+
diff --git a/connection/__init__.py b/connection/__init__.py
new file mode 100644
index 0000000..15c7b3f
--- /dev/null
+++ b/connection/__init__.py
@@ -0,0 +1,2 @@
+from .HTTPLIB2Capture import HTTPLIB2Capture
+from .ModelPredictionsReceiver import ModelPredictionsReceiver
\ No newline at end of file
diff --git a/get_predictions.py b/get_predictions.py
deleted file mode 100644
index 65e824f..0000000
--- a/get_predictions.py
+++ /dev/null
@@ -1,82 +0,0 @@
-import requests
-import numpy as np
-from logging import Logger
-
-PORT = 5000
-
-def predict_human(img: np.array, server_url: str, logger: Logger):
-    try:
-        response = requests.post(
-                f"{server_url}:{PORT}/predict_human",
-                json={
-                    "image": img.tolist()
-                }
-            )
-    except Exception as exc:
-        logger.critical(
-             "Cannot send request. Error - {}".format(exc)
-        )
-        return [None, None]
-    status_code = response.status_code
-    if status_code == 200:
-        n_boxes = response.json().get('n_boxes')
-        coordinates = np.array(response.json().get("coordinates"))
-    else:
-         logger.warning(
-              "Response code = {}.\n response = {}".format(status_code, response)
-         )
-         n_boxes = None
-         coordinates = None
-    return [n_boxes, coordinates]
-    
-def predict_bottles(img: np.array, server_url: str, logger: Logger):
-    try:
-        response = requests.post(
-                f"{server_url}:{PORT}/predict_bottles",
-                json={
-                    "image": img.tolist()
-                }
-            )
-    except Exception as exc:
-        logger.critical(
-             "Cannot send request. Error - {}".format(exc)
-        )
-        return [None, None]
-    status_code = response.status_code
-    if status_code == 200:
-        n_bottles = response.json().get('n_items')
-        coordinates = np.array(response.json().get("coordinates"))
-    else:
-         logger.warning(
-              "Response code = {}.\n response = {}".format(status_code, response)
-         )
-         n_bottles = None
-         coordinates = None
-    return [n_bottles, coordinates]
-
-def predict_boxes(img: np.array, server_url: str, logger: Logger):
-        try:
-            response = requests.post(
-                f"{server_url}:{PORT}/predict_boxes",
-                json={
-                    "image": img.tolist()
-                }
-            )
-        except Exception as exc:
-            logger.critical(
-                "Cannot send request. Error - {}".format(exc)
-            )
-            return [None, None]
-        status_code = response.status_code
-        if status_code == 200:
-            n_boxes = response.json().get('n_items')
-            coordinates = np.array(response.json().get("coordinates"))
-        else:
-            logger.warning(
-                "Response code = {}.\n response = {}".format(status_code, response)
-            )
-            n_boxes = None
-            coordinates = None
-        return [n_boxes, coordinates]
-
-
diff --git a/main.py b/main.py
index bbdc3e7..15de8b4 100644
--- a/main.py
+++ b/main.py
@@ -1,4 +1,4 @@
-from min_max_utils.HTTPLIB2Capture import HTTPLIB2Capture
+from connection import HTTPLIB2Capture
 from min_max_utils.min_max_utils import create_logger
 import warnings
 import os
diff --git a/min_max_utils/MinMaxReporter.py b/min_max_utils/MinMaxReporter.py
index 45c0982..d0278e7 100644
--- a/min_max_utils/MinMaxReporter.py
+++ b/min_max_utils/MinMaxReporter.py
@@ -39,7 +39,7 @@ def add_empty_zone(zones: list):
         return zones
 
     def create_report(self, n_boxes_history: list, img: np.array, areas: list, boxes_coords: list, zones: list) -> dict:
-        red_lines = find_red_line(img)
+        red_lines = find_red_line(img) 
         report = []
         if not zones:
             for item in areas:
@@ -67,13 +67,12 @@ def create_report(self, n_boxes_history: list, img: np.array, areas: list, boxes
             for subarr_idx, coord in enumerate(item['coords']):
                 area_coords = convert_coords_from_dict_to_list(coord)
 
-                is_red_line_in_subarea = False
-                for idx, line in enumerate(red_lines):
-                    if is_line_in_area(area_coords, line):
-                        debug_user_image = draw_line(debug_user_image, line, area_coords, thickness=4)
-                        is_red_line_in_subarea = is_red_line_in_item = True
-
                 if multi_row:
+                    is_red_line_in_subarea = False
+                    for idx, line in enumerate(red_lines):
+                        if is_line_in_area(area_coords, line):
+                            debug_user_image = draw_line(debug_user_image, line, area_coords, thickness=4)
+                            is_red_line_in_subarea = is_red_line_in_item = True
                     text_item = f"{item_name}: {n_boxes_history[item_index][subarr_idx] if not is_red_line_in_subarea else 'low stock level'}"
                 else:
                     text_item = f"{item_name}: "
diff --git a/min_max_utils/min_max_utils.py b/min_max_utils/min_max_utils.py
index f5f0724..730d4e3 100644
--- a/min_max_utils/min_max_utils.py
+++ b/min_max_utils/min_max_utils.py
@@ -109,7 +109,7 @@ def check_box_in_area(box_coord, area_coord):
     return False
 
 
-def filter_boxes(main_item_coords, _, boxes_coords, area_coords=None, check=True):
+def filter_boxes(main_item_coords, boxes_coords, area_coords=None, check=True):
     result = []
     for box_coord in boxes_coords:
         box_coord = transfer_coords(box_coord, main_item_coords)
diff --git a/model_image/ObjectDetectionModel.py b/model_image/ObjectDetectionModel.py
index 4194c0e..7f5ccd2 100644
--- a/model_image/ObjectDetectionModel.py
+++ b/model_image/ObjectDetectionModel.py
@@ -1,8 +1,9 @@
 from ultralytics import YOLO
 import torch
+import numpy as np
 
 
-class ObjDetectionModel:
+class YOLOv8ObjDetectionModel:
     def __init__(self, path: str, conf_thresh: float, iou_thresh: float, classes: list) -> None:
         self.model = YOLO(path)
         self.conf_thresh = conf_thresh
@@ -10,7 +11,7 @@ def __init__(self, path: str, conf_thresh: float, iou_thresh: float, classes: li
         self.classes = classes
 
     @torch.no_grad()
-    def __call__(self, img, classes: list = None) -> list:
+    def __call__(self, img: np.array, classes: list = None) -> list:
         results = self.model(
             source=img,
             conf=self.conf_thresh,
@@ -19,7 +20,6 @@ def __call__(self, img, classes: list = None) -> list:
             classes=self.classes if classes is None else classes,
             verbose=False
         )[0].boxes
-        n_boxes = len(results)
         coords_with_confs = torch.hstack((results.xyxy, results.conf.unsqueeze(-1)))
-        return [n_boxes, coords_with_confs]
+        return coords_with_confs
     
\ No newline at end of file
diff --git a/model_image/YOLORObjectDetectionModel.py b/model_image/YOLORObjectDetectionModel.py
new file mode 100644
index 0000000..8f368b2
--- /dev/null
+++ b/model_image/YOLORObjectDetectionModel.py
@@ -0,0 +1,34 @@
+import torch
+from yolor.model import get_model
+import numpy as np
+from yolor.utils.datasets import letterbox
+from yolor.utils.general import non_max_suppression, scale_coords
+
+
+class YOLORObjectDetectionModel:
+    def __init__(self, model_path: str, config_path: str, conf_thresh, iou_thresh, classes) -> None:
+        self.model, self.device = get_model(model_path, config_path)
+        self.conf_thresh = conf_thresh
+        self.iou_thresh = iou_thresh
+        self.classes = classes
+
+    def __preprocess_image__(self, img: np.array) -> np.array:
+        self.img_shape = img.shape
+        img = letterbox(img.copy(), new_shape=1280, auto_size=64)[0]
+        img = img[:, :, ::-1].transpose(2, 0, 1)
+        img = np.ascontiguousarray(img)
+        img = torch.from_numpy(img).to(self.device)
+        img = img.float()
+        img /= 255.0
+        img = img.unsqueeze(0)
+        return img
+
+    @torch.no_grad()
+    def __call__(self, img: np.array) -> list:
+        img = self.__preprocess_image__(img)
+        pred = self.model(img, augment=False)[0]
+        pred = non_max_suppression(
+            pred, 0.45, 0.5, classes=self.classes, agnostic=False)[0]
+        pred[:, :4] = scale_coords(
+            img.shape[2:], pred[:, :4], self.img_shape).round()
+        return pred[:, :5]
diff --git a/model_image/app.py b/model_image/app.py
index f543432..d35455e 100644
--- a/model_image/app.py
+++ b/model_image/app.py
@@ -1,16 +1,18 @@
 from PIL import Image
 from flask import Flask, jsonify, request
 from flask_configs.load_configs import *
-from ObjectDetectionModel import ObjDetectionModel
+from ObjectDetectionModel import YOLOv8ObjDetectionModel
+from YOLORObjectDetectionModel import YOLORObjectDetectionModel
 import numpy as np
 import colorlog
 import logging
+import io
 
 
 app = Flask(__name__)
-human_model = ObjDetectionModel(HUMAN_MODEL_PATH, CONF_THRES, IOU_THRES, CLASSES)
-box_model = ObjDetectionModel(BOX_MODEL_PATH, CONF_THRES, IOU_THRES, CLASSES)
-bottle_model = ObjDetectionModel(BOTTLE_MODEL_PATH, CONF_THRES, IOU_THRES, CLASSES)
+human_model = YOLORObjectDetectionModel(HUMAN_MODEL_PATH, CONF_PATH, CONF_THRES, IOU_THRES, CLASSES)
+box_model = YOLOv8ObjDetectionModel(BOX_MODEL_PATH, CONF_THRES, IOU_THRES, CLASSES)
+bottle_model = YOLORObjectDetectionModel(HUMAN_MODEL_PATH, CONF_PATH, 0.25, IOU_THRES, [39])
 
 
 logger = logging.getLogger('min_max_logger')
@@ -28,14 +30,16 @@
 logger.setLevel(logging.DEBUG)
 logger.propagate = False
 
+convert_bytes2image = lambda bytes: np.array(Image.open(io.BytesIO(bytes)), dtype=np.uint8)
+
 @app.route('/predict_human', methods=['POST'])
 def predict_human():
     if request.method == 'POST':
-        image = np.array(request.json['image']).astype(np.float32)
-        n_boxes, coords = human_model(image)
+        image = convert_bytes2image(request.files["image"].read()).astype(np.float32)
+        coords = human_model(image)
+        logger.info(f"request to predict_human: {len(coords)}")
         return jsonify(
             {
-                "n_boxes": n_boxes,
                 "coordinates": coords.tolist()
             }
         )
@@ -43,12 +47,11 @@ def predict_human():
 @app.route('/predict_boxes', methods=['POST'])
 def predict_boxes():
     if request.method == 'POST':
-        image = np.array(request.json['image']).astype(np.float32)
-        n_boxes, coords = box_model(image)
-        logger.info("Request to predict_boxes: " + str(n_boxes))
+        image = convert_bytes2image(request.files["image"].read()).astype(np.float32)
+        coords = box_model(image)
+        logger.info(f"request to predict_boxes: {len(coords)}")
         return jsonify(
             {
-                "n_items": n_boxes,
                 "coordinates": coords.tolist()
             }
         )
@@ -56,12 +59,11 @@ def predict_boxes():
 @app.route('/predict_bottles', methods=['POST'])
 def predict_bottles():
     if request.method == 'POST':
-        image = np.array(request.json['image']).astype(np.float32)
-        n_bottles, coords = bottle_model(image)
-        logger.info("Request to predict_bottles: " + str(n_bottles))
+        image = convert_bytes2image(request.files["image"].read()).astype(np.float32)
+        coords = bottle_model(image)
+        logger.info(f"request to predict_bottles: {len(coords)}")
         return jsonify(
             {
-                "n_items": n_bottles,
                 "coordinates": coords.tolist()
             }
         )
diff --git a/model_image/flask_configs/flask_confs.json b/model_image/flask_configs/flask_confs.json
index d8a86cc..f67796f 100644
--- a/model_image/flask_configs/flask_confs.json
+++ b/model_image/flask_configs/flask_confs.json
@@ -4,9 +4,9 @@
     ],
     "iou_thres": 0.5,
     "conf_thres": 0.46,
-    "box_detect_model": "min_max_v0.3.10.pt",
-    "human_detect_model": "min_max_v1.0h.pt",
-    "bottle_detect_model": "min_max_v0.4.3b.pt",
+    "config_path": "weights/yolor_csp_x.cfg",
+    "box_detect_model": "weights/min_max_v0.3.10.pt",
+    "human_detect_model": "weights/min_max_v0.4.4b.pt",
     "img_size": 640,
     "port": 5000
 }
\ No newline at end of file
diff --git a/model_image/flask_configs/load_configs.py b/model_image/flask_configs/load_configs.py
index 3e1170f..bcf3de6 100644
--- a/model_image/flask_configs/load_configs.py
+++ b/model_image/flask_configs/load_configs.py
@@ -7,7 +7,7 @@
     IOU_THRES = configs.get("iou_thres")
     BOX_MODEL_PATH = configs.get("box_detect_model")
     HUMAN_MODEL_PATH = configs.get("human_detect_model")
-    BOTTLE_MODEL_PATH = configs.get("bottle_detect_model")
+    CONF_PATH = configs.get("config_path")
     CLASSES = configs.get("classes")
     IMG_SIZE = configs.get("img_size")
     PORT = configs.get("port")
diff --git a/model_image/min_max_v0.3.10.pt b/model_image/min_max_v0.3.10.pt
deleted file mode 100644
index 215bfc8..0000000
--- a/model_image/min_max_v0.3.10.pt
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:3215d2bd185cf71226f086c6fe968787a2595d9233a96ac9ec3c98833b8cab3d
-size 87638462
diff --git a/model_image/min_max_v1.0h.pt b/model_image/min_max_v1.0h.pt
deleted file mode 100644
index 5d0bece..0000000
--- a/model_image/min_max_v1.0h.pt
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:31e20dde3def09e2cf938c7be6fe23d9150bbbe503982af13345706515f2ef95
-size 6534387
diff --git a/model_image/yolor/__init__.py b/model_image/yolor/__init__.py
new file mode 100644
index 0000000..04d37b3
--- /dev/null
+++ b/model_image/yolor/__init__.py
@@ -0,0 +1 @@
+from .model import get_model
\ No newline at end of file
diff --git a/model_image/yolor/model.py b/model_image/yolor/model.py
new file mode 100644
index 0000000..bd49e64
--- /dev/null
+++ b/model_image/yolor/model.py
@@ -0,0 +1,12 @@
+import torch
+from .utils.torch_utils import select_device
+from .models.models import Darknet
+
+
+def get_model(weights, cfg):
+    imgsz = 1280
+    device = select_device('cpu')
+    model = Darknet(cfg, imgsz)
+    model.load_state_dict(torch.load(weights, map_location=device)['model'])
+    model.to(device).eval()
+    return model, device
diff --git a/model_image/yolor/models/__init__.py b/model_image/yolor/models/__init__.py
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/model_image/yolor/models/__init__.py
@@ -0,0 +1 @@
+
diff --git a/model_image/yolor/models/export.py b/model_image/yolor/models/export.py
new file mode 100644
index 0000000..f96920b
--- /dev/null
+++ b/model_image/yolor/models/export.py
@@ -0,0 +1,68 @@
+import argparse
+
+import torch
+
+from yolor.utils.google_utils import attempt_download
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--weights', type=str, default='./yolov4.pt', help='weights path')
+    parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='image size')
+    parser.add_argument('--batch-size', type=int, default=1, help='batch size')
+    opt = parser.parse_args()
+    opt.img_size *= 2 if len(opt.img_size) == 1 else 1  # expand
+    print(opt)
+
+    # Input
+    img = torch.zeros((opt.batch_size, 3, *opt.img_size))  # image size(1,3,320,192) iDetection
+
+    # Load PyTorch model
+    attempt_download(opt.weights)
+    model = torch.load(opt.weights, map_location=torch.device('cpu'))['model'].float()
+    model.eval()
+    model.model[-1].export = True  # set Detect() layer export=True
+    y = model(img)  # dry run
+
+    # TorchScript export
+    try:
+        print('\nStarting TorchScript export with torch %s...' % torch.__version__)
+        f = opt.weights.replace('.pt', '.torchscript.pt')  # filename
+        ts = torch.jit.trace(model, img)
+        ts.save(f)
+        print('TorchScript export success, saved as %s' % f)
+    except Exception as e:
+        print('TorchScript export failure: %s' % e)
+
+    # ONNX export
+    try:
+        import onnx
+
+        print('\nStarting ONNX export with onnx %s...' % onnx.__version__)
+        f = opt.weights.replace('.pt', '.onnx')  # filename
+        model.fuse()  # only for ONNX
+        torch.onnx.export(model, img, f, verbose=False, opset_version=12, input_names=['images'],
+                          output_names=['classes', 'boxes'] if y is None else ['output'])
+
+        # Checks
+        onnx_model = onnx.load(f)  # load onnx model
+        onnx.checker.check_model(onnx_model)  # check onnx model
+        print(onnx.helper.printable_graph(onnx_model.graph))  # print a human readable model
+        print('ONNX export success, saved as %s' % f)
+    except Exception as e:
+        print('ONNX export failure: %s' % e)
+
+    # CoreML export
+    try:
+        import coremltools as ct
+
+        print('\nStarting CoreML export with coremltools %s...' % ct.__version__)
+        # convert model from torchscript and apply pixel scaling as per detect.py
+        model = ct.convert(ts, inputs=[ct.ImageType(name='images', shape=img.shape, scale=1 / 255.0, bias=[0, 0, 0])])
+        f = opt.weights.replace('.pt', '.mlmodel')  # filename
+        model.save(f)
+        print('CoreML export success, saved as %s' % f)
+    except Exception as e:
+        print('CoreML export failure: %s' % e)
+
+    # Finish
+    print('\nExport complete. Visualize with https://github.com/lutzroeder/netron.')
diff --git a/model_image/yolor/models/models.py b/model_image/yolor/models/models.py
new file mode 100644
index 0000000..cde2435
--- /dev/null
+++ b/model_image/yolor/models/models.py
@@ -0,0 +1,761 @@
+from yolor.utils.google_utils import *
+from yolor.utils.layers import *
+from yolor.utils.parse_config import *
+from yolor.utils import torch_utils
+
+ONNX_EXPORT = False
+
+
+def create_modules(module_defs, img_size, cfg):
+    # Constructs module list of layer blocks from module configuration in module_defs
+
+    img_size = [img_size] * 2 if isinstance(img_size, int) else img_size  # expand if necessary
+    _ = module_defs.pop(0)  # cfg training hyperparams (unused)
+    output_filters = [3]  # input channels
+    module_list = nn.ModuleList()
+    routs = []  # list of layers which rout to deeper layers
+    yolo_index = -1
+
+    for i, mdef in enumerate(module_defs):
+        modules = nn.Sequential()
+
+        if mdef['type'] == 'convolutional':
+            bn = mdef['batch_normalize']
+            filters = mdef['filters']
+            k = mdef['size']  # kernel size
+            stride = mdef['stride'] if 'stride' in mdef else (mdef['stride_y'], mdef['stride_x'])
+            if isinstance(k, int):  # single-size conv
+                modules.add_module('Conv2d', nn.Conv2d(in_channels=output_filters[-1],
+                                                       out_channels=filters,
+                                                       kernel_size=k,
+                                                       stride=stride,
+                                                       padding=k // 2 if mdef['pad'] else 0,
+                                                       groups=mdef['groups'] if 'groups' in mdef else 1,
+                                                       bias=not bn))
+            else:  # multiple-size conv
+                modules.add_module('MixConv2d', MixConv2d(in_ch=output_filters[-1],
+                                                          out_ch=filters,
+                                                          k=k,
+                                                          stride=stride,
+                                                          bias=not bn))
+
+            if bn:
+                modules.add_module('BatchNorm2d', nn.BatchNorm2d(filters, momentum=0.03, eps=1E-4))
+            else:
+                routs.append(i)  # detection output (goes into yolo layer)
+
+            if mdef['activation'] == 'leaky':  # activation study https://github.com/ultralytics/yolov3/issues/441
+                modules.add_module('activation', nn.LeakyReLU(0.1, inplace=True))
+            elif mdef['activation'] == 'swish':
+                modules.add_module('activation', Swish())
+            elif mdef['activation'] == 'mish':
+                modules.add_module('activation', Mish())
+            elif mdef['activation'] == 'emb':
+                modules.add_module('activation', F.normalize())
+            elif mdef['activation'] == 'logistic':
+                modules.add_module('activation', nn.Sigmoid())
+            elif mdef['activation'] == 'silu':
+                modules.add_module('activation', nn.SiLU())
+
+        elif mdef['type'] == 'deformableconvolutional':
+            bn = mdef['batch_normalize']
+            filters = mdef['filters']
+            k = mdef['size']  # kernel size
+            stride = mdef['stride'] if 'stride' in mdef else (mdef['stride_y'], mdef['stride_x'])
+            if isinstance(k, int):  # single-size conv
+                modules.add_module('DeformConv2d', DeformConv2d(output_filters[-1],
+                                                       filters,
+                                                       kernel_size=k,
+                                                       padding=k // 2 if mdef['pad'] else 0,
+                                                       stride=stride,
+                                                       bias=not bn,
+                                                       modulation=True))
+            else:  # multiple-size conv
+                modules.add_module('MixConv2d', MixConv2d(in_ch=output_filters[-1],
+                                                          out_ch=filters,
+                                                          k=k,
+                                                          stride=stride,
+                                                          bias=not bn))
+
+            if bn:
+                modules.add_module('BatchNorm2d', nn.BatchNorm2d(filters, momentum=0.03, eps=1E-4))
+            else:
+                routs.append(i)  # detection output (goes into yolo layer)
+
+            if mdef['activation'] == 'leaky':  # activation study https://github.com/ultralytics/yolov3/issues/441
+                modules.add_module('activation', nn.LeakyReLU(0.1, inplace=True))
+            elif mdef['activation'] == 'swish':
+                modules.add_module('activation', Swish())
+            elif mdef['activation'] == 'mish':
+                modules.add_module('activation', Mish())
+            elif mdef['activation'] == 'silu':
+                modules.add_module('activation', nn.SiLU())
+                
+        elif mdef['type'] == 'dropout':
+            p = mdef['probability']
+            modules = nn.Dropout(p)
+
+        elif mdef['type'] == 'avgpool':
+            modules = GAP()
+
+        elif mdef['type'] == 'silence':
+            filters = output_filters[-1]
+            modules = Silence()
+
+        elif mdef['type'] == 'scale_channels':  # nn.Sequential() placeholder for 'shortcut' layer
+            layers = mdef['from']
+            filters = output_filters[-1]
+            routs.extend([i + l if l < 0 else l for l in layers])
+            modules = ScaleChannel(layers=layers)
+
+        elif mdef['type'] == 'shift_channels':  # nn.Sequential() placeholder for 'shortcut' layer
+            layers = mdef['from']
+            filters = output_filters[-1]
+            routs.extend([i + l if l < 0 else l for l in layers])
+            modules = ShiftChannel(layers=layers)
+
+        elif mdef['type'] == 'shift_channels_2d':  # nn.Sequential() placeholder for 'shortcut' layer
+            layers = mdef['from']
+            filters = output_filters[-1]
+            routs.extend([i + l if l < 0 else l for l in layers])
+            modules = ShiftChannel2D(layers=layers)
+
+        elif mdef['type'] == 'control_channels':  # nn.Sequential() placeholder for 'shortcut' layer
+            layers = mdef['from']
+            filters = output_filters[-1]
+            routs.extend([i + l if l < 0 else l for l in layers])
+            modules = ControlChannel(layers=layers)
+
+        elif mdef['type'] == 'control_channels_2d':  # nn.Sequential() placeholder for 'shortcut' layer
+            layers = mdef['from']
+            filters = output_filters[-1]
+            routs.extend([i + l if l < 0 else l for l in layers])
+            modules = ControlChannel2D(layers=layers)
+
+        elif mdef['type'] == 'alternate_channels':  # nn.Sequential() placeholder for 'shortcut' layer
+            layers = mdef['from']
+            filters = output_filters[-1] * 2
+            routs.extend([i + l if l < 0 else l for l in layers])
+            modules = AlternateChannel(layers=layers)
+
+        elif mdef['type'] == 'alternate_channels_2d':  # nn.Sequential() placeholder for 'shortcut' layer
+            layers = mdef['from']
+            filters = output_filters[-1] * 2
+            routs.extend([i + l if l < 0 else l for l in layers])
+            modules = AlternateChannel2D(layers=layers)
+
+        elif mdef['type'] == 'select_channels':  # nn.Sequential() placeholder for 'shortcut' layer
+            layers = mdef['from']
+            filters = output_filters[-1]
+            routs.extend([i + l if l < 0 else l for l in layers])
+            modules = SelectChannel(layers=layers)
+
+        elif mdef['type'] == 'select_channels_2d':  # nn.Sequential() placeholder for 'shortcut' layer
+            layers = mdef['from']
+            filters = output_filters[-1]
+            routs.extend([i + l if l < 0 else l for l in layers])
+            modules = SelectChannel2D(layers=layers)
+
+        elif mdef['type'] == 'sam':  # nn.Sequential() placeholder for 'shortcut' layer
+            layers = mdef['from']
+            filters = output_filters[-1]
+            routs.extend([i + l if l < 0 else l for l in layers])
+            modules = ScaleSpatial(layers=layers)
+
+        elif mdef['type'] == 'BatchNorm2d':
+            filters = output_filters[-1]
+            modules = nn.BatchNorm2d(filters, momentum=0.03, eps=1E-4)
+            if i == 0 and filters == 3:  # normalize RGB image
+                # imagenet mean and var https://pytorch.org/docs/stable/torchvision/models.html#classification
+                modules.running_mean = torch.tensor([0.485, 0.456, 0.406])
+                modules.running_var = torch.tensor([0.0524, 0.0502, 0.0506])
+
+        elif mdef['type'] == 'maxpool':
+            k = mdef['size']  # kernel size
+            stride = mdef['stride']
+            maxpool = nn.MaxPool2d(kernel_size=k, stride=stride, padding=(k - 1) // 2)
+            if k == 2 and stride == 1:  # yolov3-tiny
+                modules.add_module('ZeroPad2d', nn.ZeroPad2d((0, 1, 0, 1)))
+                modules.add_module('MaxPool2d', maxpool)
+            else:
+                modules = maxpool
+
+        elif mdef['type'] == 'local_avgpool':
+            k = mdef['size']  # kernel size
+            stride = mdef['stride']
+            avgpool = nn.AvgPool2d(kernel_size=k, stride=stride, padding=(k - 1) // 2)
+            if k == 2 and stride == 1:  # yolov3-tiny
+                modules.add_module('ZeroPad2d', nn.ZeroPad2d((0, 1, 0, 1)))
+                modules.add_module('AvgPool2d', avgpool)
+            else:
+                modules = avgpool
+
+        elif mdef['type'] == 'upsample':
+            if ONNX_EXPORT:  # explicitly state size, avoid scale_factor
+                g = (yolo_index + 1) * 2 / 32  # gain
+                modules = nn.Upsample(size=tuple(int(x * g) for x in img_size))  # img_size = (320, 192)
+            else:
+                modules = nn.Upsample(scale_factor=mdef['stride'])
+
+        elif mdef['type'] == 'route':  # nn.Sequential() placeholder for 'route' layer
+            layers = mdef['layers']
+            filters = sum([output_filters[l + 1 if l > 0 else l] for l in layers])
+            routs.extend([i + l if l < 0 else l for l in layers])
+            modules = FeatureConcat(layers=layers)
+
+        elif mdef['type'] == 'route2':  # nn.Sequential() placeholder for 'route' layer
+            layers = mdef['layers']
+            filters = sum([output_filters[l + 1 if l > 0 else l] for l in layers])
+            routs.extend([i + l if l < 0 else l for l in layers])
+            modules = FeatureConcat2(layers=layers)
+
+        elif mdef['type'] == 'route3':  # nn.Sequential() placeholder for 'route' layer
+            layers = mdef['layers']
+            filters = sum([output_filters[l + 1 if l > 0 else l] for l in layers])
+            routs.extend([i + l if l < 0 else l for l in layers])
+            modules = FeatureConcat3(layers=layers)
+
+        elif mdef['type'] == 'route_lhalf':  # nn.Sequential() placeholder for 'route' layer
+            layers = mdef['layers']
+            filters = sum([output_filters[l + 1 if l > 0 else l] for l in layers])//2
+            routs.extend([i + l if l < 0 else l for l in layers])
+            modules = FeatureConcat_l(layers=layers)
+
+        elif mdef['type'] == 'shortcut':  # nn.Sequential() placeholder for 'shortcut' layer
+            layers = mdef['from']
+            filters = output_filters[-1]
+            routs.extend([i + l if l < 0 else l for l in layers])
+            modules = WeightedFeatureFusion(layers=layers, weight='weights_type' in mdef)
+
+        elif mdef['type'] == 'reorg3d':  # yolov3-spp-pan-scale
+            pass
+
+        elif mdef['type'] == 'reorg':  # yolov3-spp-pan-scale
+            filters = 4 * output_filters[-1]
+            modules.add_module('Reorg', Reorg())
+
+        elif mdef['type'] == 'dwt':  # yolov3-spp-pan-scale
+            filters = 4 * output_filters[-1]
+            modules.add_module('DWT', DWT())
+
+        elif mdef['type'] == 'implicit_add':  # yolov3-spp-pan-scale
+            filters = mdef['filters']
+            modules = ImplicitA(channel=filters)
+
+        elif mdef['type'] == 'implicit_mul':  # yolov3-spp-pan-scale
+            filters = mdef['filters']
+            modules = ImplicitM(channel=filters)
+
+        elif mdef['type'] == 'implicit_cat':  # yolov3-spp-pan-scale
+            filters = mdef['filters']
+            modules = ImplicitC(channel=filters)
+
+        elif mdef['type'] == 'implicit_add_2d':  # yolov3-spp-pan-scale
+            channels = mdef['filters']
+            filters = mdef['atoms']
+            modules = Implicit2DA(atom=filters, channel=channels)
+
+        elif mdef['type'] == 'implicit_mul_2d':  # yolov3-spp-pan-scale
+            channels = mdef['filters']
+            filters = mdef['atoms']
+            modules = Implicit2DM(atom=filters, channel=channels)
+
+        elif mdef['type'] == 'implicit_cat_2d':  # yolov3-spp-pan-scale
+            channels = mdef['filters']
+            filters = mdef['atoms']
+            modules = Implicit2DC(atom=filters, channel=channels)
+
+        elif mdef['type'] == 'yolo':
+            yolo_index += 1
+            stride = [8, 16, 32, 64, 128]  # P3, P4, P5, P6, P7 strides
+            if any(x in cfg for x in ['yolov4-tiny', 'fpn', 'yolov3']):  # P5, P4, P3 strides
+                stride = [32, 16, 8]
+            layers = mdef['from'] if 'from' in mdef else []
+            modules = YOLOLayer(anchors=mdef['anchors'][mdef['mask']],  # anchor list
+                                nc=mdef['classes'],  # number of classes
+                                img_size=img_size,  # (416, 416)
+                                yolo_index=yolo_index,  # 0, 1, 2...
+                                layers=layers,  # output layers
+                                stride=stride[yolo_index])
+
+            # Initialize preceding Conv2d() bias (https://arxiv.org/pdf/1708.02002.pdf section 3.3)
+            try:
+                j = layers[yolo_index] if 'from' in mdef else -2
+                bias_ = module_list[j][0].bias  # shape(255,)
+                bias = bias_[:modules.no * modules.na].view(modules.na, -1)  # shape(3,85)
+                #bias[:, 4] += -4.5  # obj
+                bias.data[:, 4] += math.log(8 / (640 / stride[yolo_index]) ** 2)  # obj (8 objects per 640 image)
+                bias.data[:, 5:] += math.log(0.6 / (modules.nc - 0.99))  # cls (sigmoid(p) = 1/nc)
+                module_list[j][0].bias = torch.nn.Parameter(bias_, requires_grad=bias_.requires_grad)
+                
+                #j = [-2, -5, -8]
+                #for sj in j:
+                #    bias_ = module_list[sj][0].bias
+                #    bias = bias_[:modules.no * 1].view(1, -1)
+                #    bias.data[:, 4] += math.log(8 / (640 / stride[yolo_index]) ** 2)
+                #    bias.data[:, 5:] += math.log(0.6 / (modules.nc - 0.99))
+                #    module_list[sj][0].bias = torch.nn.Parameter(bias_, requires_grad=bias_.requires_grad)
+            except:
+                print('WARNING: smart bias initialization failure.')
+
+        elif mdef['type'] == 'jde':
+            yolo_index += 1
+            stride = [8, 16, 32, 64, 128]  # P3, P4, P5, P6, P7 strides
+            if any(x in cfg for x in ['yolov4-tiny', 'fpn', 'yolov3']):  # P5, P4, P3 strides
+                stride = [32, 16, 8]
+            layers = mdef['from'] if 'from' in mdef else []
+            modules = JDELayer(anchors=mdef['anchors'][mdef['mask']],  # anchor list
+                                nc=mdef['classes'],  # number of classes
+                                img_size=img_size,  # (416, 416)
+                                yolo_index=yolo_index,  # 0, 1, 2...
+                                layers=layers,  # output layers
+                                stride=stride[yolo_index])
+
+            # Initialize preceding Conv2d() bias (https://arxiv.org/pdf/1708.02002.pdf section 3.3)
+            try:
+                j = layers[yolo_index] if 'from' in mdef else -1
+                bias_ = module_list[j][0].bias  # shape(255,)
+                bias = bias_[:modules.no * modules.na].view(modules.na, -1)  # shape(3,85)
+                #bias[:, 4] += -4.5  # obj
+                bias.data[:, 4] += math.log(8 / (640 / stride[yolo_index]) ** 2)  # obj (8 objects per 640 image)
+                bias.data[:, 5:] += math.log(0.6 / (modules.nc - 0.99))  # cls (sigmoid(p) = 1/nc)
+                module_list[j][0].bias = torch.nn.Parameter(bias_, requires_grad=bias_.requires_grad)
+            except:
+                print('WARNING: smart bias initialization failure.')
+
+        else:
+            print('Warning: Unrecognized Layer Type: ' + mdef['type'])
+
+        # Register module list and number of output filters
+        module_list.append(modules)
+        output_filters.append(filters)
+
+    routs_binary = [False] * (i + 1)
+    for i in routs:
+        routs_binary[i] = True
+    return module_list, routs_binary
+
+
+class YOLOLayer(nn.Module):
+    def __init__(self, anchors, nc, img_size, yolo_index, layers, stride):
+        super(YOLOLayer, self).__init__()
+        self.anchors = torch.Tensor(anchors)
+        self.index = yolo_index  # index of this layer in layers
+        self.layers = layers  # model output layer indices
+        self.stride = stride  # layer stride
+        self.nl = len(layers)  # number of output layers (3)
+        self.na = len(anchors)  # number of anchors (3)
+        self.nc = nc  # number of classes (80)
+        self.no = nc + 5  # number of outputs (85)
+        self.nx, self.ny, self.ng = 0, 0, 0  # initialize number of x, y gridpoints
+        self.anchor_vec = self.anchors / self.stride
+        self.anchor_wh = self.anchor_vec.view(1, self.na, 1, 1, 2)
+
+        if ONNX_EXPORT:
+            self.training = False
+            self.create_grids((img_size[1] // stride, img_size[0] // stride))  # number x, y grid points
+
+    def create_grids(self, ng=(13, 13), device='cpu'):
+        self.nx, self.ny = ng  # x and y grid size
+        self.ng = torch.tensor(ng, dtype=torch.float)
+
+        # build xy offsets
+        if not self.training:
+            yv, xv = torch.meshgrid([torch.arange(self.ny, device=device), torch.arange(self.nx, device=device)])
+            self.grid = torch.stack((xv, yv), 2).view((1, 1, self.ny, self.nx, 2)).float()
+
+        if self.anchor_vec.device != device:
+            self.anchor_vec = self.anchor_vec.to(device)
+            self.anchor_wh = self.anchor_wh.to(device)
+
+    def forward(self, p, out):
+        ASFF = False  # https://arxiv.org/abs/1911.09516
+        if ASFF:
+            i, n = self.index, self.nl  # index in layers, number of layers
+            p = out[self.layers[i]]
+            bs, _, ny, nx = p.shape  # bs, 255, 13, 13
+            if (self.nx, self.ny) != (nx, ny):
+                self.create_grids((nx, ny), p.device)
+
+            # outputs and weights
+            # w = F.softmax(p[:, -n:], 1)  # normalized weights
+            w = torch.sigmoid(p[:, -n:]) * (2 / n)  # sigmoid weights (faster)
+            # w = w / w.sum(1).unsqueeze(1)  # normalize across layer dimension
+
+            # weighted ASFF sum
+            p = out[self.layers[i]][:, :-n] * w[:, i:i + 1]
+            for j in range(n):
+                if j != i:
+                    p += w[:, j:j + 1] * \
+                         F.interpolate(out[self.layers[j]][:, :-n], size=[ny, nx], mode='bilinear', align_corners=False)
+
+        elif ONNX_EXPORT:
+            bs = 1  # batch size
+        else:
+            bs, _, ny, nx = p.shape  # bs, 255, 13, 13
+            if (self.nx, self.ny) != (nx, ny):
+                self.create_grids((nx, ny), p.device)
+
+        # p.view(bs, 255, 13, 13) -- > (bs, 3, 13, 13, 85)  # (bs, anchors, grid, grid, classes + xywh)
+        p = p.view(bs, self.na, self.no, self.ny, self.nx).permute(0, 1, 3, 4, 2).contiguous()  # prediction
+
+        if self.training:
+            return p
+
+        elif ONNX_EXPORT:
+            # Avoid broadcasting for ANE operations
+            m = self.na * self.nx * self.ny
+            ng = 1. / self.ng.repeat(m, 1)
+            grid = self.grid.repeat(1, self.na, 1, 1, 1).view(m, 2)
+            anchor_wh = self.anchor_wh.repeat(1, 1, self.nx, self.ny, 1).view(m, 2) * ng
+
+            p = p.view(m, self.no)
+            xy = torch.sigmoid(p[:, 0:2]) + grid  # x, y
+            wh = torch.exp(p[:, 2:4]) * anchor_wh  # width, height
+            p_cls = torch.sigmoid(p[:, 4:5]) if self.nc == 1 else \
+                torch.sigmoid(p[:, 5:self.no]) * torch.sigmoid(p[:, 4:5])  # conf
+            return p_cls, xy * ng, wh
+
+        else:  # inference
+            io = p.sigmoid()
+            io[..., :2] = (io[..., :2] * 2. - 0.5 + self.grid)
+            io[..., 2:4] = (io[..., 2:4] * 2) ** 2 * self.anchor_wh
+            io[..., :4] *= self.stride
+            #io = p.clone()  # inference output
+            #io[..., :2] = torch.sigmoid(io[..., :2]) + self.grid  # xy
+            #io[..., 2:4] = torch.exp(io[..., 2:4]) * self.anchor_wh  # wh yolo method
+            #io[..., :4] *= self.stride
+            #torch.sigmoid_(io[..., 4:])
+            return io.view(bs, -1, self.no), p  # view [1, 3, 13, 13, 85] as [1, 507, 85]
+
+
+class JDELayer(nn.Module):
+    def __init__(self, anchors, nc, img_size, yolo_index, layers, stride):
+        super(JDELayer, self).__init__()
+        self.anchors = torch.Tensor(anchors)
+        self.index = yolo_index  # index of this layer in layers
+        self.layers = layers  # model output layer indices
+        self.stride = stride  # layer stride
+        self.nl = len(layers)  # number of output layers (3)
+        self.na = len(anchors)  # number of anchors (3)
+        self.nc = nc  # number of classes (80)
+        self.no = nc + 5  # number of outputs (85)
+        self.nx, self.ny, self.ng = 0, 0, 0  # initialize number of x, y gridpoints
+        self.anchor_vec = self.anchors / self.stride
+        self.anchor_wh = self.anchor_vec.view(1, self.na, 1, 1, 2)
+
+        if ONNX_EXPORT:
+            self.training = False
+            self.create_grids((img_size[1] // stride, img_size[0] // stride))  # number x, y grid points
+
+    def create_grids(self, ng=(13, 13), device='cpu'):
+        self.nx, self.ny = ng  # x and y grid size
+        self.ng = torch.tensor(ng, dtype=torch.float)
+
+        # build xy offsets
+        if not self.training:
+            yv, xv = torch.meshgrid([torch.arange(self.ny, device=device), torch.arange(self.nx, device=device)])
+            self.grid = torch.stack((xv, yv), 2).view((1, 1, self.ny, self.nx, 2)).float()
+
+        if self.anchor_vec.device != device:
+            self.anchor_vec = self.anchor_vec.to(device)
+            self.anchor_wh = self.anchor_wh.to(device)
+
+    def forward(self, p, out):
+        ASFF = False  # https://arxiv.org/abs/1911.09516
+        if ASFF:
+            i, n = self.index, self.nl  # index in layers, number of layers
+            p = out[self.layers[i]]
+            bs, _, ny, nx = p.shape  # bs, 255, 13, 13
+            if (self.nx, self.ny) != (nx, ny):
+                self.create_grids((nx, ny), p.device)
+
+            # outputs and weights
+            # w = F.softmax(p[:, -n:], 1)  # normalized weights
+            w = torch.sigmoid(p[:, -n:]) * (2 / n)  # sigmoid weights (faster)
+            # w = w / w.sum(1).unsqueeze(1)  # normalize across layer dimension
+
+            # weighted ASFF sum
+            p = out[self.layers[i]][:, :-n] * w[:, i:i + 1]
+            for j in range(n):
+                if j != i:
+                    p += w[:, j:j + 1] * \
+                         F.interpolate(out[self.layers[j]][:, :-n], size=[ny, nx], mode='bilinear', align_corners=False)
+
+        elif ONNX_EXPORT:
+            bs = 1  # batch size
+        else:
+            bs, _, ny, nx = p.shape  # bs, 255, 13, 13
+            if (self.nx, self.ny) != (nx, ny):
+                self.create_grids((nx, ny), p.device)
+
+        # p.view(bs, 255, 13, 13) -- > (bs, 3, 13, 13, 85)  # (bs, anchors, grid, grid, classes + xywh)
+        p = p.view(bs, self.na, self.no, self.ny, self.nx).permute(0, 1, 3, 4, 2).contiguous()  # prediction
+
+        if self.training:
+            return p
+
+        elif ONNX_EXPORT:
+            # Avoid broadcasting for ANE operations
+            m = self.na * self.nx * self.ny
+            ng = 1. / self.ng.repeat(m, 1)
+            grid = self.grid.repeat(1, self.na, 1, 1, 1).view(m, 2)
+            anchor_wh = self.anchor_wh.repeat(1, 1, self.nx, self.ny, 1).view(m, 2) * ng
+
+            p = p.view(m, self.no)
+            xy = torch.sigmoid(p[:, 0:2]) + grid  # x, y
+            wh = torch.exp(p[:, 2:4]) * anchor_wh  # width, height
+            p_cls = torch.sigmoid(p[:, 4:5]) if self.nc == 1 else \
+                torch.sigmoid(p[:, 5:self.no]) * torch.sigmoid(p[:, 4:5])  # conf
+            return p_cls, xy * ng, wh
+
+        else:  # inference
+            #io = p.sigmoid()
+            #io[..., :2] = (io[..., :2] * 2. - 0.5 + self.grid)
+            #io[..., 2:4] = (io[..., 2:4] * 2) ** 2 * self.anchor_wh
+            #io[..., :4] *= self.stride
+            io = p.clone()  # inference output
+            io[..., :2] = torch.sigmoid(io[..., :2]) * 2. - 0.5 + self.grid  # xy
+            io[..., 2:4] = (torch.sigmoid(io[..., 2:4]) * 2) ** 2 * self.anchor_wh  # wh yolo method
+            io[..., :4] *= self.stride
+            io[..., 4:] = F.softmax(io[..., 4:])
+            return io.view(bs, -1, self.no), p  # view [1, 3, 13, 13, 85] as [1, 507, 85]
+
+class Darknet(nn.Module):
+    # YOLOv3 object detection model
+
+    def __init__(self, cfg, img_size=(416, 416), verbose=False):
+        super(Darknet, self).__init__()
+
+        self.module_defs = parse_model_cfg(cfg)
+        self.module_list, self.routs = create_modules(self.module_defs, img_size, cfg)
+        self.yolo_layers = get_yolo_layers(self)
+        # torch_utils.initialize_weights(self)
+
+        # Darknet Header https://github.com/AlexeyAB/darknet/issues/2914#issuecomment-496675346
+        self.version = np.array([0, 2, 5], dtype=np.int32)  # (int32) version info: major, minor, revision
+        self.seen = np.array([0], dtype=np.int64)  # (int64) number of images seen during training
+        self.info(verbose) if not ONNX_EXPORT else None  # print model description
+
+    def forward(self, x, augment=False, verbose=False):
+
+        if not augment:
+            return self.forward_once(x)
+        else:  # Augment images (inference and test only) https://github.com/ultralytics/yolov3/issues/931
+            img_size = x.shape[-2:]  # height, width
+            s = [0.83, 0.67]  # scales
+            y = []
+            for i, xi in enumerate((x,
+                                    torch_utils.scale_img(x.flip(3), s[0], same_shape=False),  # flip-lr and scale
+                                    torch_utils.scale_img(x, s[1], same_shape=False),  # scale
+                                    )):
+                # cv2.imwrite('img%g.jpg' % i, 255 * xi[0].numpy().transpose((1, 2, 0))[:, :, ::-1])
+                y.append(self.forward_once(xi)[0])
+
+            y[1][..., :4] /= s[0]  # scale
+            y[1][..., 0] = img_size[1] - y[1][..., 0]  # flip lr
+            y[2][..., :4] /= s[1]  # scale
+
+            # for i, yi in enumerate(y):  # coco small, medium, large = < 32**2 < 96**2 <
+            #     area = yi[..., 2:4].prod(2)[:, :, None]
+            #     if i == 1:
+            #         yi *= (area < 96. ** 2).float()
+            #     elif i == 2:
+            #         yi *= (area > 32. ** 2).float()
+            #     y[i] = yi
+
+            y = torch.cat(y, 1)
+            return y, None
+
+    def forward_once(self, x, augment=False, verbose=False):
+        img_size = x.shape[-2:]  # height, width
+        yolo_out, out = [], []
+        if verbose:
+            print('0', x.shape)
+            str = ''
+
+        # Augment images (inference and test only)
+        if augment:  # https://github.com/ultralytics/yolov3/issues/931
+            nb = x.shape[0]  # batch size
+            s = [0.83, 0.67]  # scales
+            x = torch.cat((x,
+                           torch_utils.scale_img(x.flip(3), s[0]),  # flip-lr and scale
+                           torch_utils.scale_img(x, s[1]),  # scale
+                           ), 0)
+
+        for i, module in enumerate(self.module_list):
+            name = module.__class__.__name__
+            #print(name)
+            if name in ['WeightedFeatureFusion', 'FeatureConcat', 'FeatureConcat2', 'FeatureConcat3', 'FeatureConcat_l', 'ScaleChannel', 'ShiftChannel', 'ShiftChannel2D', 'ControlChannel', 'ControlChannel2D', 'AlternateChannel', 'AlternateChannel2D', 'SelectChannel', 'SelectChannel2D', 'ScaleSpatial']:  # sum, concat
+                if verbose:
+                    l = [i - 1] + module.layers  # layers
+                    sh = [list(x.shape)] + [list(out[i].shape) for i in module.layers]  # shapes
+                    str = ' >> ' + ' + '.join(['layer %g %s' % x for x in zip(l, sh)])
+                x = module(x, out)  # WeightedFeatureFusion(), FeatureConcat()
+            elif name in ['ImplicitA', 'ImplicitM', 'ImplicitC', 'Implicit2DA', 'Implicit2DM', 'Implicit2DC']:
+                x = module()
+            elif name == 'YOLOLayer':
+                yolo_out.append(module(x, out))
+            elif name == 'JDELayer':
+                yolo_out.append(module(x, out))
+            else:  # run module directly, i.e. mtype = 'convolutional', 'upsample', 'maxpool', 'batchnorm2d' etc.
+                #print(module)
+                #print(x.shape)
+                x = module(x)
+
+            out.append(x if self.routs[i] else [])
+            if verbose:
+                print('%g/%g %s -' % (i, len(self.module_list), name), list(x.shape), str)
+                str = ''
+
+        if self.training:  # train
+            return yolo_out
+        elif ONNX_EXPORT:  # export
+            x = [torch.cat(x, 0) for x in zip(*yolo_out)]
+            return x[0], torch.cat(x[1:3], 1)  # scores, boxes: 3780x80, 3780x4
+        else:  # inference or test
+            x, p = zip(*yolo_out)  # inference output, training output
+            x = torch.cat(x, 1)  # cat yolo outputs
+            if augment:  # de-augment results
+                x = torch.split(x, nb, dim=0)
+                x[1][..., :4] /= s[0]  # scale
+                x[1][..., 0] = img_size[1] - x[1][..., 0]  # flip lr
+                x[2][..., :4] /= s[1]  # scale
+                x = torch.cat(x, 1)
+            return x, p
+
+    def fuse(self):
+        # Fuse Conv2d + BatchNorm2d layers throughout model
+        print('Fusing layers...')
+        fused_list = nn.ModuleList()
+        for a in list(self.children())[0]:
+            if isinstance(a, nn.Sequential):
+                for i, b in enumerate(a):
+                    if isinstance(b, nn.modules.batchnorm.BatchNorm2d):
+                        # fuse this bn layer with the previous conv2d layer
+                        conv = a[i - 1]
+                        fused = torch_utils.fuse_conv_and_bn(conv, b)
+                        a = nn.Sequential(fused, *list(a.children())[i + 1:])
+                        break
+            fused_list.append(a)
+        self.module_list = fused_list
+        self.info() if not ONNX_EXPORT else None  # yolov3-spp reduced from 225 to 152 layers
+
+    def info(self, verbose=False):
+        torch_utils.model_info(self, verbose)
+
+
+def get_yolo_layers(model):
+    return [i for i, m in enumerate(model.module_list) if m.__class__.__name__ in ['YOLOLayer', 'JDELayer']]  # [89, 101, 113]
+
+
+def load_darknet_weights(self, weights, cutoff=-1):
+    # Parses and loads the weights stored in 'weights'
+
+    # Establish cutoffs (load layers between 0 and cutoff. if cutoff = -1 all are loaded)
+    file = Path(weights).name
+    if file == 'darknet53.conv.74':
+        cutoff = 75
+    elif file == 'yolov3-tiny.conv.15':
+        cutoff = 15
+
+    # Read weights file
+    with open(weights, 'rb') as f:
+        # Read Header https://github.com/AlexeyAB/darknet/issues/2914#issuecomment-496675346
+        self.version = np.fromfile(f, dtype=np.int32, count=3)  # (int32) version info: major, minor, revision
+        self.seen = np.fromfile(f, dtype=np.int64, count=1)  # (int64) number of images seen during training
+
+        weights = np.fromfile(f, dtype=np.float32)  # the rest are weights
+
+    ptr = 0
+    for i, (mdef, module) in enumerate(zip(self.module_defs[:cutoff], self.module_list[:cutoff])):
+        if mdef['type'] == 'convolutional':
+            conv = module[0]
+            if mdef['batch_normalize']:
+                # Load BN bias, weights, running mean and running variance
+                bn = module[1]
+                nb = bn.bias.numel()  # number of biases
+                # Bias
+                bn.bias.data.copy_(torch.from_numpy(weights[ptr:ptr + nb]).view_as(bn.bias))
+                ptr += nb
+                # Weight
+                bn.weight.data.copy_(torch.from_numpy(weights[ptr:ptr + nb]).view_as(bn.weight))
+                ptr += nb
+                # Running Mean
+                bn.running_mean.data.copy_(torch.from_numpy(weights[ptr:ptr + nb]).view_as(bn.running_mean))
+                ptr += nb
+                # Running Var
+                bn.running_var.data.copy_(torch.from_numpy(weights[ptr:ptr + nb]).view_as(bn.running_var))
+                ptr += nb
+            else:
+                # Load conv. bias
+                nb = conv.bias.numel()
+                conv_b = torch.from_numpy(weights[ptr:ptr + nb]).view_as(conv.bias)
+                conv.bias.data.copy_(conv_b)
+                ptr += nb
+            # Load conv. weights
+            nw = conv.weight.numel()  # number of weights
+            conv.weight.data.copy_(torch.from_numpy(weights[ptr:ptr + nw]).view_as(conv.weight))
+            ptr += nw
+
+
+def save_weights(self, path='model.weights', cutoff=-1):
+    # Converts a PyTorch model to Darket format (*.pt to *.weights)
+    # Note: Does not work if model.fuse() is applied
+    with open(path, 'wb') as f:
+        # Write Header https://github.com/AlexeyAB/darknet/issues/2914#issuecomment-496675346
+        self.version.tofile(f)  # (int32) version info: major, minor, revision
+        self.seen.tofile(f)  # (int64) number of images seen during training
+
+        # Iterate through layers
+        for i, (mdef, module) in enumerate(zip(self.module_defs[:cutoff], self.module_list[:cutoff])):
+            if mdef['type'] == 'convolutional':
+                conv_layer = module[0]
+                # If batch norm, load bn first
+                if mdef['batch_normalize']:
+                    bn_layer = module[1]
+                    bn_layer.bias.data.cpu().numpy().tofile(f)
+                    bn_layer.weight.data.cpu().numpy().tofile(f)
+                    bn_layer.running_mean.data.cpu().numpy().tofile(f)
+                    bn_layer.running_var.data.cpu().numpy().tofile(f)
+                # Load conv bias
+                else:
+                    conv_layer.bias.data.cpu().numpy().tofile(f)
+                # Load conv weights
+                conv_layer.weight.data.cpu().numpy().tofile(f)
+
+
+def convert(cfg='cfg/yolov3-spp.cfg', weights='weights/yolov3-spp.weights', saveto='converted.weights'):
+    # Converts between PyTorch and Darknet format per extension (i.e. *.weights convert to *.pt and vice versa)
+    # from models import *; convert('cfg/yolov3-spp.cfg', 'weights/yolov3-spp.weights')
+
+    # Initialize model
+    model = Darknet(cfg)
+    ckpt = torch.load(weights)  # load checkpoint
+    try:
+        ckpt['model'] = {k: v for k, v in ckpt['model'].items() if model.state_dict()[k].numel() == v.numel()}
+        model.load_state_dict(ckpt['model'], strict=False)
+        save_weights(model, path=saveto, cutoff=-1)
+    except KeyError as e:
+        print(e)
+
+def attempt_download(weights):
+    # Attempt to download pretrained weights if not found locally
+    weights = weights.strip()
+    msg = weights + ' missing, try downloading from https://drive.google.com/open?id=1LezFG5g3BCW6iYaV89B2i64cqEUZD7e0'
+
+    if len(weights) > 0 and not os.path.isfile(weights):
+        d = {''}
+
+        file = Path(weights).name
+        if file in d:
+            r = gdrive_download(id=d[file], name=weights)
+        else:  # download from pjreddie.com
+            url = 'https://pjreddie.com/media/files/' + file
+            print('Downloading ' + url)
+            r = os.system('curl -f ' + url + ' -o ' + weights)
+
+        # Error check
+        if not (r == 0 and os.path.exists(weights) and os.path.getsize(weights) > 1E6):  # weights exist and > 1MB
+            os.system('rm ' + weights)  # remove partial downloads
+            raise Exception(msg)
diff --git a/model_image/yolor/utils/__init__.py b/model_image/yolor/utils/__init__.py
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/model_image/yolor/utils/__init__.py
@@ -0,0 +1 @@
+
diff --git a/model_image/yolor/utils/activations.py b/model_image/yolor/utils/activations.py
new file mode 100644
index 0000000..ba6b854
--- /dev/null
+++ b/model_image/yolor/utils/activations.py
@@ -0,0 +1,72 @@
+# Activation functions
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+# Swish https://arxiv.org/pdf/1905.02244.pdf ---------------------------------------------------------------------------
+class Swish(nn.Module):  #
+    @staticmethod
+    def forward(x):
+        return x * torch.sigmoid(x)
+
+
+class Hardswish(nn.Module):  # export-friendly version of nn.Hardswish()
+    @staticmethod
+    def forward(x):
+        # return x * F.hardsigmoid(x)  # for torchscript and CoreML
+        return x * F.hardtanh(x + 3, 0., 6.) / 6.  # for torchscript, CoreML and ONNX
+
+
+class MemoryEfficientSwish(nn.Module):
+    class F(torch.autograd.Function):
+        @staticmethod
+        def forward(ctx, x):
+            ctx.save_for_backward(x)
+            return x * torch.sigmoid(x)
+
+        @staticmethod
+        def backward(ctx, grad_output):
+            x = ctx.saved_tensors[0]
+            sx = torch.sigmoid(x)
+            return grad_output * (sx * (1 + x * (1 - sx)))
+
+    def forward(self, x):
+        return self.F.apply(x)
+
+
+# Mish https://github.com/digantamisra98/Mish --------------------------------------------------------------------------
+class Mish(nn.Module):
+    @staticmethod
+    def forward(x):
+        return x * F.softplus(x).tanh()
+
+
+class MemoryEfficientMish(nn.Module):
+    class F(torch.autograd.Function):
+        @staticmethod
+        def forward(ctx, x):
+            ctx.save_for_backward(x)
+            return x.mul(torch.tanh(F.softplus(x)))  # x * tanh(ln(1 + exp(x)))
+
+        @staticmethod
+        def backward(ctx, grad_output):
+            x = ctx.saved_tensors[0]
+            sx = torch.sigmoid(x)
+            fx = F.softplus(x).tanh()
+            return grad_output * (fx + x * sx * (1 - fx * fx))
+
+    def forward(self, x):
+        return self.F.apply(x)
+
+
+# FReLU https://arxiv.org/abs/2007.11824 -------------------------------------------------------------------------------
+class FReLU(nn.Module):
+    def __init__(self, c1, k=3):  # ch_in, kernel
+        super().__init__()
+        self.conv = nn.Conv2d(c1, c1, k, 1, 1, groups=c1)
+        self.bn = nn.BatchNorm2d(c1)
+
+    def forward(self, x):
+        return torch.max(x, self.bn(self.conv(x)))
diff --git a/model_image/yolor/utils/autoanchor.py b/model_image/yolor/utils/autoanchor.py
new file mode 100644
index 0000000..1e82492
--- /dev/null
+++ b/model_image/yolor/utils/autoanchor.py
@@ -0,0 +1,152 @@
+# Auto-anchor utils
+
+import numpy as np
+import torch
+import yaml
+from scipy.cluster.vq import kmeans
+from tqdm import tqdm
+
+
+def check_anchor_order(m):
+    # Check anchor order against stride order for YOLOv5 Detect() module m, and correct if necessary
+    a = m.anchor_grid.prod(-1).view(-1)  # anchor area
+    da = a[-1] - a[0]  # delta a
+    ds = m.stride[-1] - m.stride[0]  # delta s
+    if da.sign() != ds.sign():  # same order
+        print('Reversing anchor order')
+        m.anchors[:] = m.anchors.flip(0)
+        m.anchor_grid[:] = m.anchor_grid.flip(0)
+
+
+def check_anchors(dataset, model, thr=4.0, imgsz=640):
+    # Check anchor fit to data, recompute if necessary
+    print('\nAnalyzing anchors... ', end='')
+    m = model.module.model[-1] if hasattr(model, 'module') else model.model[-1]  # Detect()
+    shapes = imgsz * dataset.shapes / dataset.shapes.max(1, keepdims=True)
+    scale = np.random.uniform(0.9, 1.1, size=(shapes.shape[0], 1))  # augment scale
+    wh = torch.tensor(np.concatenate([l[:, 3:5] * s for s, l in zip(shapes * scale, dataset.labels)])).float()  # wh
+
+    def metric(k):  # compute metric
+        r = wh[:, None] / k[None]
+        x = torch.min(r, 1. / r).min(2)[0]  # ratio metric
+        best = x.max(1)[0]  # best_x
+        aat = (x > 1. / thr).float().sum(1).mean()  # anchors above threshold
+        bpr = (best > 1. / thr).float().mean()  # best possible recall
+        return bpr, aat
+
+    bpr, aat = metric(m.anchor_grid.clone().cpu().view(-1, 2))
+    print('anchors/target = %.2f, Best Possible Recall (BPR) = %.4f' % (aat, bpr), end='')
+    if bpr < 0.98:  # threshold to recompute
+        print('. Attempting to improve anchors, please wait...')
+        na = m.anchor_grid.numel() // 2  # number of anchors
+        new_anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False)
+        new_bpr = metric(new_anchors.reshape(-1, 2))[0]
+        if new_bpr > bpr:  # replace anchors
+            new_anchors = torch.tensor(new_anchors, device=m.anchors.device).type_as(m.anchors)
+            m.anchor_grid[:] = new_anchors.clone().view_as(m.anchor_grid)  # for inference
+            m.anchors[:] = new_anchors.clone().view_as(m.anchors) / m.stride.to(m.anchors.device).view(-1, 1, 1)  # loss
+            check_anchor_order(m)
+            print('New anchors saved to model. Update model *.yaml to use these anchors in the future.')
+        else:
+            print('Original anchors better than new anchors. Proceeding with original anchors.')
+    print('')  # newline
+
+
+def kmean_anchors(path='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=1000, verbose=True):
+    """ Creates kmeans-evolved anchors from training dataset
+
+        Arguments:
+            path: path to dataset *.yaml, or a loaded dataset
+            n: number of anchors
+            img_size: image size used for training
+            thr: anchor-label wh ratio threshold hyperparameter hyp['anchor_t'] used for training, default=4.0
+            gen: generations to evolve anchors using genetic algorithm
+            verbose: print all results
+
+        Return:
+            k: kmeans evolved anchors
+
+        Usage:
+            from utils.general import *; _ = kmean_anchors()
+    """
+    thr = 1. / thr
+
+    def metric(k, wh):  # compute metrics
+        r = wh[:, None] / k[None]
+        x = torch.min(r, 1. / r).min(2)[0]  # ratio metric
+        # x = wh_iou(wh, torch.tensor(k))  # iou metric
+        return x, x.max(1)[0]  # x, best_x
+
+    def anchor_fitness(k):  # mutation fitness
+        _, best = metric(torch.tensor(k, dtype=torch.float32), wh)
+        return (best * (best > thr).float()).mean()  # fitness
+
+    def print_results(k):
+        k = k[np.argsort(k.prod(1))]  # sort small to large
+        x, best = metric(k, wh0)
+        bpr, aat = (best > thr).float().mean(), (x > thr).float().mean() * n  # best possible recall, anch > thr
+        print('thr=%.2f: %.4f best possible recall, %.2f anchors past thr' % (thr, bpr, aat))
+        print('n=%g, img_size=%s, metric_all=%.3f/%.3f-mean/best, past_thr=%.3f-mean: ' %
+              (n, img_size, x.mean(), best.mean(), x[x > thr].mean()), end='')
+        for i, x in enumerate(k):
+            print('%i,%i' % (round(x[0]), round(x[1])), end=',  ' if i < len(k) - 1 else '\n')  # use in *.cfg
+        return k
+
+    if isinstance(path, str):  # *.yaml file
+        with open(path) as f:
+            data_dict = yaml.load(f, Loader=yaml.FullLoader)  # model dict
+        from utils.datasets import LoadImagesAndLabels
+        dataset = LoadImagesAndLabels(data_dict['train'], augment=True, rect=True)
+    else:
+        dataset = path  # dataset
+
+    # Get label wh
+    shapes = img_size * dataset.shapes / dataset.shapes.max(1, keepdims=True)
+    wh0 = np.concatenate([l[:, 3:5] * s for s, l in zip(shapes, dataset.labels)])  # wh
+
+    # Filter
+    i = (wh0 < 3.0).any(1).sum()
+    if i:
+        print('WARNING: Extremely small objects found. '
+              '%g of %g labels are < 3 pixels in width or height.' % (i, len(wh0)))
+    wh = wh0[(wh0 >= 2.0).any(1)]  # filter > 2 pixels
+
+    # Kmeans calculation
+    print('Running kmeans for %g anchors on %g points...' % (n, len(wh)))
+    s = wh.std(0)  # sigmas for whitening
+    k, dist = kmeans(wh / s, n, iter=30)  # points, mean distance
+    k *= s
+    wh = torch.tensor(wh, dtype=torch.float32)  # filtered
+    wh0 = torch.tensor(wh0, dtype=torch.float32)  # unfiltered
+    k = print_results(k)
+
+    # Plot
+    # k, d = [None] * 20, [None] * 20
+    # for i in tqdm(range(1, 21)):
+    #     k[i-1], d[i-1] = kmeans(wh / s, i)  # points, mean distance
+    # fig, ax = plt.subplots(1, 2, figsize=(14, 7))
+    # ax = ax.ravel()
+    # ax[0].plot(np.arange(1, 21), np.array(d) ** 2, marker='.')
+    # fig, ax = plt.subplots(1, 2, figsize=(14, 7))  # plot wh
+    # ax[0].hist(wh[wh[:, 0]<100, 0],400)
+    # ax[1].hist(wh[wh[:, 1]<100, 1],400)
+    # fig.tight_layout()
+    # fig.savefig('wh.png', dpi=200)
+
+    # Evolve
+    npr = np.random
+    f, sh, mp, s = anchor_fitness(k), k.shape, 0.9, 0.1  # fitness, generations, mutation prob, sigma
+    pbar = tqdm(range(gen), desc='Evolving anchors with Genetic Algorithm')  # progress bar
+    for _ in pbar:
+        v = np.ones(sh)
+        while (v == 1).all():  # mutate until a change occurs (prevent duplicates)
+            v = ((npr.random(sh) < mp) * npr.random() * npr.randn(*sh) * s + 1).clip(0.3, 3.0)
+        kg = (k.copy() * v).clip(min=2.0)
+        fg = anchor_fitness(kg)
+        if fg > f:
+            f, k = fg, kg.copy()
+            pbar.desc = 'Evolving anchors with Genetic Algorithm: fitness = %.4f' % f
+            if verbose:
+                print_results(k)
+
+    return print_results(k)
diff --git a/model_image/yolor/utils/datasets.py b/model_image/yolor/utils/datasets.py
new file mode 100644
index 0000000..96aeb7d
--- /dev/null
+++ b/model_image/yolor/utils/datasets.py
@@ -0,0 +1,1399 @@
+# Dataset utils and dataloaders
+
+import glob
+import math
+import os
+import random
+import shutil
+import time
+from itertools import repeat
+from multiprocessing.pool import ThreadPool
+from pathlib import Path
+from threading import Thread
+
+import cv2
+import numpy as np
+import torch
+from PIL import Image, ExifTags
+from torch.utils.data import Dataset
+
+from yolor.utils.general import xyxy2xywh, xywh2xyxy
+from yolor.utils.torch_utils import torch_distributed_zero_first
+
+# Parameters
+help_url = 'https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data'
+img_formats = ['bmp', 'jpg', 'jpeg', 'png', 'tif',
+               'tiff', 'dng']  # acceptable image suffixes
+vid_formats = ['mov', 'avi', 'mp4', 'mpg', 'mpeg',
+               'm4v', 'wmv', 'mkv']  # acceptable video suffixes
+
+# Get orientation exif tag
+for orientation in ExifTags.TAGS.keys():
+    if ExifTags.TAGS[orientation] == 'Orientation':
+        break
+
+
+def get_hash(files):
+    # Returns a single hash value of a list of files
+    return sum(os.path.getsize(f) for f in files if os.path.isfile(f))
+
+
+def exif_size(img):
+    # Returns exif-corrected PIL size
+    s = img.size  # (width, height)
+    try:
+        rotation = dict(img._getexif().items())[orientation]
+        if rotation == 6:  # rotation 270
+            s = (s[1], s[0])
+        elif rotation == 8:  # rotation 90
+            s = (s[1], s[0])
+    except:
+        pass
+
+    return s
+
+
+def create_dataloader(path, imgsz, batch_size, stride, opt, hyp=None, augment=False, cache=False, pad=0.0, rect=False,
+                      rank=-1, world_size=1, workers=8):
+    # Make sure only the first process in DDP process the dataset first, and the following others can use the cache
+    with torch_distributed_zero_first(rank):
+        dataset = LoadImagesAndLabels(path, imgsz, batch_size,
+                                      augment=augment,  # augment images
+                                      hyp=hyp,  # augmentation hyperparameters
+                                      rect=rect,  # rectangular training
+                                      cache_images=cache,
+                                      single_cls=opt.single_cls,
+                                      stride=int(stride),
+                                      pad=pad,
+                                      rank=rank)
+
+    batch_size = min(batch_size, len(dataset))
+    nw = min([os.cpu_count() // world_size, batch_size if batch_size >
+             1 else 0, workers])  # number of workers
+    sampler = torch.utils.data.distributed.DistributedSampler(
+        dataset) if rank != -1 else None
+    dataloader = InfiniteDataLoader(dataset,
+                                    batch_size=batch_size,
+                                    num_workers=nw,
+                                    sampler=sampler,
+                                    pin_memory=True,
+                                    collate_fn=LoadImagesAndLabels.collate_fn)  # torch.utils.data.DataLoader()
+    return dataloader, dataset
+
+
+def create_dataloader9(path, imgsz, batch_size, stride, opt, hyp=None, augment=False, cache=False, pad=0.0, rect=False,
+                       rank=-1, world_size=1, workers=8):
+    # Make sure only the first process in DDP process the dataset first, and the following others can use the cache
+    with torch_distributed_zero_first(rank):
+        dataset = LoadImagesAndLabels9(path, imgsz, batch_size,
+                                       augment=augment,  # augment images
+                                       hyp=hyp,  # augmentation hyperparameters
+                                       rect=rect,  # rectangular training
+                                       cache_images=cache,
+                                       single_cls=opt.single_cls,
+                                       stride=int(stride),
+                                       pad=pad,
+                                       rank=rank)
+
+    batch_size = min(batch_size, len(dataset))
+    nw = min([os.cpu_count() // world_size, batch_size if batch_size >
+             1 else 0, workers])  # number of workers
+    sampler = torch.utils.data.distributed.DistributedSampler(
+        dataset) if rank != -1 else None
+    dataloader = InfiniteDataLoader(dataset,
+                                    batch_size=batch_size,
+                                    num_workers=nw,
+                                    sampler=sampler,
+                                    pin_memory=True,
+                                    collate_fn=LoadImagesAndLabels9.collate_fn)  # torch.utils.data.DataLoader()
+    return dataloader, dataset
+
+
+class InfiniteDataLoader(torch.utils.data.dataloader.DataLoader):
+    """ Dataloader that reuses workers
+
+    Uses same syntax as vanilla DataLoader
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        object.__setattr__(self, 'batch_sampler',
+                           _RepeatSampler(self.batch_sampler))
+        self.iterator = super().__iter__()
+
+    def __len__(self):
+        return len(self.batch_sampler.sampler)
+
+    def __iter__(self):
+        for i in range(len(self)):
+            yield next(self.iterator)
+
+
+class _RepeatSampler(object):
+    """ Sampler that repeats forever
+
+    Args:
+        sampler (Sampler)
+    """
+
+    def __init__(self, sampler):
+        self.sampler = sampler
+
+    def __iter__(self):
+        while True:
+            yield from iter(self.sampler)
+
+
+class LoadImages:  # for inference
+    def __init__(self, path, img_size=640, auto_size=32):
+        p = str(Path(path))  # os-agnostic
+        p = os.path.abspath(p)  # absolute path
+        if '*' in p:
+            files = sorted(glob.glob(p, recursive=True))  # glob
+        elif os.path.isdir(p):
+            files = sorted(glob.glob(os.path.join(p, '*.*')))  # dir
+        elif os.path.isfile(p):
+            files = [p]  # files
+        else:
+            raise Exception('ERROR: %s does not exist' % p)
+
+        images = [x for x in files if x.split('.')[-1].lower() in img_formats]
+        videos = [x for x in files if x.split('.')[-1].lower() in vid_formats]
+        ni, nv = len(images), len(videos)
+
+        self.img_size = img_size
+        self.auto_size = auto_size
+        self.files = images + videos
+        self.nf = ni + nv  # number of files
+        self.video_flag = [False] * ni + [True] * nv
+        self.mode = 'images'
+        if any(videos):
+            self.new_video(videos[0])  # new video
+        else:
+            self.cap = None
+        assert self.nf > 0, 'No images or videos found in %s. Supported formats are:\nimages: %s\nvideos: %s' % \
+                            (p, img_formats, vid_formats)
+
+    def __iter__(self):
+        self.count = 0
+        return self
+
+    def __next__(self):
+        if self.count == self.nf:
+            raise StopIteration
+        path = self.files[self.count]
+
+        if self.video_flag[self.count]:
+            # Read video
+            self.mode = 'video'
+            ret_val, img0 = self.cap.read()
+            if not ret_val:
+                self.count += 1
+                self.cap.release()
+                if self.count == self.nf:  # last video
+                    raise StopIteration
+                else:
+                    path = self.files[self.count]
+                    self.new_video(path)
+                    ret_val, img0 = self.cap.read()
+
+            self.frame += 1
+            print('video %g/%g (%g/%g) %s: ' % (self.count + 1,
+                  self.nf, self.frame, self.nframes, path), end='')
+
+        else:
+            # Read image
+            self.count += 1
+            img0 = cv2.imread(path)  # BGR
+            assert img0 is not None, 'Image Not Found ' + path
+            print('image %g/%g %s: ' % (self.count, self.nf, path), end='')
+
+        # Padded resize
+        img = letterbox(img0, new_shape=self.img_size,
+                        auto_size=self.auto_size)[0]
+
+        # Convert
+        img = img[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416
+        img = np.ascontiguousarray(img)
+
+        return path, img, img0, self.cap
+
+    def new_video(self, path):
+        self.frame = 0
+        self.cap = cv2.VideoCapture(path)
+        self.nframes = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT))
+
+    def __len__(self):
+        return self.nf  # number of files
+
+
+class LoadWebcam:  # for inference
+    def __init__(self, pipe='0', img_size=640):
+        self.img_size = img_size
+
+        if pipe.isnumeric():
+            pipe = eval(pipe)  # local camera
+        # pipe = 'rtsp://192.168.1.64/1'  # IP camera
+        # pipe = 'rtsp://username:password@192.168.1.64/1'  # IP camera with login
+        # pipe = 'http://wmccpinetop.axiscam.net/mjpg/video.mjpg'  # IP golf camera
+
+        self.pipe = pipe
+        self.cap = cv2.VideoCapture(pipe)  # video capture object
+        self.cap.set(cv2.CAP_PROP_BUFFERSIZE, 3)  # set buffer size
+
+    def __iter__(self):
+        self.count = -1
+        return self
+
+    def __next__(self):
+        self.count += 1
+        if cv2.waitKey(1) == ord('q'):  # q to quit
+            self.cap.release()
+            cv2.destroyAllWindows()
+            raise StopIteration
+
+        # Read frame
+        if self.pipe == 0:  # local camera
+            ret_val, img0 = self.cap.read()
+            img0 = cv2.flip(img0, 1)  # flip left-right
+        else:  # IP camera
+            n = 0
+            while True:
+                n += 1
+                self.cap.grab()
+                if n % 30 == 0:  # skip frames
+                    ret_val, img0 = self.cap.retrieve()
+                    if ret_val:
+                        break
+
+        # Print
+        assert ret_val, 'Camera Error %s' % self.pipe
+        img_path = 'webcam.jpg'
+        print('webcam %g: ' % self.count, end='')
+
+        # Padded resize
+        img = letterbox(img0, new_shape=self.img_size)[0]
+
+        # Convert
+        img = img[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416
+        img = np.ascontiguousarray(img)
+
+        return img_path, img, img0, None
+
+    def __len__(self):
+        return 0
+
+
+class LoadStreams:  # multiple IP or RTSP cameras
+    def __init__(self, sources='streams.txt', img_size=640):
+        self.mode = 'images'
+        self.img_size = img_size
+
+        if os.path.isfile(sources):
+            with open(sources, 'r') as f:
+                sources = [x.strip()
+                           for x in f.read().splitlines() if len(x.strip())]
+        else:
+            sources = [sources]
+
+        n = len(sources)
+        self.imgs = [None] * n
+        self.sources = sources
+        for i, s in enumerate(sources):
+            # Start the thread to read frames from the video stream
+            print('%g/%g: %s... ' % (i + 1, n, s), end='')
+            cap = cv2.VideoCapture(eval(s) if s.isnumeric() else s)
+            assert cap.isOpened(), 'Failed to open %s' % s
+            w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+            h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+            fps = cap.get(cv2.CAP_PROP_FPS) % 100
+            _, self.imgs[i] = cap.read()  # guarantee first frame
+            thread = Thread(target=self.update, args=([i, cap]), daemon=True)
+            print(' success (%gx%g at %.2f FPS).' % (w, h, fps))
+            thread.start()
+        print('')  # newline
+
+        # check for common shapes
+        s = np.stack([letterbox(x, new_shape=self.img_size)[
+                     0].shape for x in self.imgs], 0)  # inference shapes
+        # rect inference if all shapes equal
+        self.rect = np.unique(s, axis=0).shape[0] == 1
+        if not self.rect:
+            print('WARNING: Different stream shapes detected. For optimal performance supply similarly-shaped streams.')
+
+    def update(self, index, cap):
+        # Read next stream frame in a daemon thread
+        n = 0
+        while cap.isOpened():
+            n += 1
+            # _, self.imgs[index] = cap.read()
+            cap.grab()
+            if n == 4:  # read every 4th frame
+                _, self.imgs[index] = cap.retrieve()
+                n = 0
+            time.sleep(0.01)  # wait time
+
+    def __iter__(self):
+        self.count = -1
+        return self
+
+    def __next__(self):
+        self.count += 1
+        img0 = self.imgs.copy()
+        if cv2.waitKey(1) == ord('q'):  # q to quit
+            cv2.destroyAllWindows()
+            raise StopIteration
+
+        # Letterbox
+        img = [letterbox(x, new_shape=self.img_size, auto=self.rect)[0]
+               for x in img0]
+
+        # Stack
+        img = np.stack(img, 0)
+
+        # Convert
+        # BGR to RGB, to bsx3x416x416
+        img = img[:, :, :, ::-1].transpose(0, 3, 1, 2)
+        img = np.ascontiguousarray(img)
+
+        return self.sources, img, img0, None
+
+    def __len__(self):
+        return 0  # 1E12 frames = 32 streams at 30 FPS for 30 years
+
+
+class LoadImagesAndLabels(Dataset):  # for training/testing
+    def __init__(self, path, img_size=640, batch_size=16, augment=False, hyp=None, rect=False, image_weights=False,
+                 cache_images=False, single_cls=False, stride=32, pad=0.0, rank=-1):
+        self.img_size = img_size
+        self.augment = augment
+        self.hyp = hyp
+        self.image_weights = image_weights
+        self.rect = False if image_weights else rect
+        # load 4 images at a time into a mosaic (only during training)
+        self.mosaic = self.augment and not self.rect
+        self.mosaic_border = [-img_size // 2, -img_size // 2]
+        self.stride = stride
+
+        def img2label_paths(img_paths):
+            # Define label paths as a function of image paths
+            sa, sb = os.sep + 'images' + os.sep, os.sep + \
+                'labels' + os.sep  # /images/, /labels/ substrings
+            return [x.replace(sa, sb, 1).replace(x.split('.')[-1], 'txt') for x in img_paths]
+
+        try:
+            f = []  # image files
+            for p in path if isinstance(path, list) else [path]:
+                p = Path(p)  # os-agnostic
+                if p.is_dir():  # dir
+                    f += glob.glob(str(p / '**' / '*.*'), recursive=True)
+                elif p.is_file():  # file
+                    with open(p, 'r') as t:
+                        t = t.read().splitlines()
+                        parent = str(p.parent) + os.sep
+                        # local to global path
+                        f += [x.replace('./', parent)
+                              if x.startswith('./') else x for x in t]
+                else:
+                    raise Exception('%s does not exist' % p)
+            self.img_files = sorted(
+                [x.replace('/', os.sep) for x in f if x.split('.')[-1].lower() in img_formats])
+            assert self.img_files, 'No images found'
+        except Exception as e:
+            raise Exception('Error loading data from %s: %s\nSee %s' %
+                            (path, e, help_url))
+
+        # Check cache
+        self.label_files = img2label_paths(self.img_files)  # labels
+        # cached labels
+        cache_path = str(Path(self.label_files[0]).parent) + '.cache3'
+        if os.path.isfile(cache_path):
+            cache = torch.load(cache_path)  # load
+            # dataset changed
+            if cache['hash'] != get_hash(self.label_files + self.img_files):
+                cache = self.cache_labels(cache_path)  # re-cache
+        else:
+            cache = self.cache_labels(cache_path)  # cache
+
+        # Read cache
+        cache.pop('hash')  # remove hash
+        labels, shapes = zip(*cache.values())
+        self.labels = list(labels)
+        self.shapes = np.array(shapes, dtype=np.float64)
+        self.img_files = list(cache.keys())  # update
+        self.label_files = img2label_paths(cache.keys())  # update
+
+        n = len(shapes)  # number of images
+        bi = np.floor(np.arange(n) / batch_size).astype(np.int)  # batch index
+        nb = bi[-1] + 1  # number of batches
+        self.batch = bi  # batch index of image
+        self.n = n
+
+        # Rectangular Training
+        if self.rect:
+            # Sort by aspect ratio
+            s = self.shapes  # wh
+            ar = s[:, 1] / s[:, 0]  # aspect ratio
+            irect = ar.argsort()
+            self.img_files = [self.img_files[i] for i in irect]
+            self.label_files = [self.label_files[i] for i in irect]
+            self.labels = [self.labels[i] for i in irect]
+            self.shapes = s[irect]  # wh
+            ar = ar[irect]
+
+            # Set training image shapes
+            shapes = [[1, 1]] * nb
+            for i in range(nb):
+                ari = ar[bi == i]
+                mini, maxi = ari.min(), ari.max()
+                if maxi < 1:
+                    shapes[i] = [maxi, 1]
+                elif mini > 1:
+                    shapes[i] = [1, 1 / mini]
+
+            self.batch_shapes = np.ceil(
+                np.array(shapes) * img_size / stride + pad).astype(np.int) * stride
+
+        # Check labels
+        create_datasubset, extract_bounding_boxes, labels_loaded = False, False, False
+        # number missing, found, empty, datasubset, duplicate
+        nm, nf, ne, ns, nd = 0, 0, 0, 0, 0
+        pbar = enumerate(self.label_files)
+        if rank in [-1, 0]:
+            pbar = tqdm(pbar)
+        for i, file in pbar:
+            l = self.labels[i]  # label
+            if l is not None and l.shape[0]:
+                assert l.shape[1] == 5, '> 5 label columns: %s' % file
+                assert (l >= 0).all(), 'negative labels: %s' % file
+                assert (l[:, 1:] <= 1).all(
+                ), 'non-normalized or out of bounds coordinate labels: %s' % file
+                if np.unique(l, axis=0).shape[0] < l.shape[0]:  # duplicate rows
+                    # print('WARNING: duplicate rows in %s' % self.label_files[i])  # duplicate rows
+                    nd += 1
+                if single_cls:
+                    l[:, 0] = 0  # force dataset into single-class mode
+                self.labels[i] = l
+                nf += 1  # file found
+
+                # Create subdataset (a smaller dataset)
+                if create_datasubset and ns < 1E4:
+                    if ns == 0:
+                        create_folder(path='./datasubset')
+                        os.makedirs('./datasubset/images')
+                    exclude_classes = 43
+                    if exclude_classes not in l[:, 0]:
+                        ns += 1
+                        # shutil.copy(src=self.img_files[i], dst='./datasubset/images/')  # copy image
+                        with open('./datasubset/images.txt', 'a') as f:
+                            f.write(self.img_files[i] + '\n')
+
+                # Extract object detection boxes for a second stage classifier
+                if extract_bounding_boxes:
+                    p = Path(self.img_files[i])
+                    img = cv2.imread(str(p))
+                    h, w = img.shape[:2]
+                    for j, x in enumerate(l):
+                        f = '%s%sclassifier%s%g_%g_%s' % (
+                            p.parent.parent, os.sep, os.sep, x[0], j, p.name)
+                        if not os.path.exists(Path(f).parent):
+                            # make new output folder
+                            os.makedirs(Path(f).parent)
+
+                        b = x[1:] * [w, h, w, h]  # box
+                        b[2:] = b[2:].max()  # rectangle to square
+                        b[2:] = b[2:] * 1.3 + 30  # pad
+                        b = xywh2xyxy(b.reshape(-1, 4)).ravel().astype(np.int)
+
+                        # clip boxes outside of image
+                        b[[0, 2]] = np.clip(b[[0, 2]], 0, w)
+                        b[[1, 3]] = np.clip(b[[1, 3]], 0, h)
+                        assert cv2.imwrite(
+                            f, img[b[1]:b[3], b[0]:b[2]]), 'Failure extracting classifier boxes'
+            else:
+                # print('empty labels for image %s' % self.img_files[i])  # file empty
+                ne += 1
+                # os.system("rm '%s' '%s'" % (self.img_files[i], self.label_files[i]))  # remove
+
+            if rank in [-1, 0]:
+                pbar.desc = 'Scanning labels %s (%g found, %g missing, %g empty, %g duplicate, for %g images)' % (
+                    cache_path, nf, nm, ne, nd, n)
+        if nf == 0:
+            s = 'WARNING: No labels found in %s. See %s' % (
+                os.path.dirname(file) + os.sep, help_url)
+            print(s)
+            assert not augment, '%s. Can not train without labels.' % s
+
+        # Cache images into memory for faster training (WARNING: large datasets may exceed system RAM)
+        self.imgs = [None] * n
+        if cache_images:
+            gb = 0  # Gigabytes of cached images
+            self.img_hw0, self.img_hw = [None] * n, [None] * n
+            results = ThreadPool(8).imap(lambda x: load_image(
+                *x), zip(repeat(self), range(n)))  # 8 threads
+            pbar = tqdm(enumerate(results), total=n)
+            for i, x in pbar:
+                # img, hw_original, hw_resized = load_image(self, i)
+                self.imgs[i], self.img_hw0[i], self.img_hw[i] = x
+                gb += self.imgs[i].nbytes
+                pbar.desc = 'Caching images (%.1fGB)' % (gb / 1E9)
+
+    def cache_labels(self, path='labels.cache3'):
+        # Cache dataset labels, check images and read shapes
+        x = {}  # dict
+        pbar = tqdm(zip(self.img_files, self.label_files),
+                    desc='Scanning images', total=len(self.img_files))
+        for (img, label) in pbar:
+            try:
+                l = []
+                im = Image.open(img)
+                im.verify()  # PIL verify
+                shape = exif_size(im)  # image size
+                assert (shape[0] > 9) & (shape[1] > 9), 'image size <10 pixels'
+                if os.path.isfile(label):
+                    with open(label, 'r') as f:
+                        l = np.array(
+                            [x.split() for x in f.read().splitlines()], dtype=np.float32)  # labels
+                if len(l) == 0:
+                    l = np.zeros((0, 5), dtype=np.float32)
+                x[img] = [l, shape]
+            except Exception as e:
+                print('WARNING: Ignoring corrupted image and/or label %s: %s' % (img, e))
+
+        x['hash'] = get_hash(self.label_files + self.img_files)
+        torch.save(x, path)  # save for next time
+        return x
+
+    def __len__(self):
+        return len(self.img_files)
+
+    # def __iter__(self):
+    #     self.count = -1
+    #     print('ran dataset iter')
+    #     #self.shuffled_vector = np.random.permutation(self.nF) if self.augment else np.arange(self.nF)
+    #     return self
+
+    def __getitem__(self, index):
+        if self.image_weights:
+            index = self.indices[index]
+
+        hyp = self.hyp
+        mosaic = self.mosaic and random.random() < hyp['mosaic']
+        if mosaic:
+            # Load mosaic
+            img, labels = load_mosaic(self, index)
+            # img, labels = load_mosaic9(self, index)
+            shapes = None
+
+            # MixUp https://arxiv.org/pdf/1710.09412.pdf
+            if random.random() < hyp['mixup']:
+                img2, labels2 = load_mosaic(
+                    self, random.randint(0, len(self.labels) - 1))
+                # img2, labels2 = load_mosaic9(self, random.randint(0, len(self.labels) - 1))
+                r = np.random.beta(8.0, 8.0)  # mixup ratio, alpha=beta=8.0
+                img = (img * r + img2 * (1 - r)).astype(np.uint8)
+                labels = np.concatenate((labels, labels2), 0)
+
+        else:
+            # Load image
+            img, (h0, w0), (h, w) = load_image(self, index)
+
+            # Letterbox
+            # final letterboxed shape
+            shape = self.batch_shapes[self.batch[index]
+                                      ] if self.rect else self.img_size
+            img, ratio, pad = letterbox(
+                img, shape, auto=False, scaleup=self.augment)
+            # for COCO mAP rescaling
+            shapes = (h0, w0), ((h / h0, w / w0), pad)
+
+            # Load labels
+            labels = []
+            x = self.labels[index]
+            if x.size > 0:
+                # Normalized xywh to pixel xyxy format
+                labels = x.copy()
+                labels[:, 1] = ratio[0] * w * \
+                    (x[:, 1] - x[:, 3] / 2) + pad[0]  # pad width
+                labels[:, 2] = ratio[1] * h * \
+                    (x[:, 2] - x[:, 4] / 2) + pad[1]  # pad height
+                labels[:, 3] = ratio[0] * w * (x[:, 1] + x[:, 3] / 2) + pad[0]
+                labels[:, 4] = ratio[1] * h * (x[:, 2] + x[:, 4] / 2) + pad[1]
+
+        if self.augment:
+            # Augment imagespace
+            if not mosaic:
+                img, labels = random_perspective(img, labels,
+                                                 degrees=hyp['degrees'],
+                                                 translate=hyp['translate'],
+                                                 scale=hyp['scale'],
+                                                 shear=hyp['shear'],
+                                                 perspective=hyp['perspective'])
+
+            # Augment colorspace
+            augment_hsv(img, hgain=hyp['hsv_h'],
+                        sgain=hyp['hsv_s'], vgain=hyp['hsv_v'])
+
+            # Apply cutouts
+            # if random.random() < 0.9:
+            #     labels = cutout(img, labels)
+
+        nL = len(labels)  # number of labels
+        if nL:
+            labels[:, 1:5] = xyxy2xywh(labels[:, 1:5])  # convert xyxy to xywh
+            labels[:, [2, 4]] /= img.shape[0]  # normalized height 0-1
+            labels[:, [1, 3]] /= img.shape[1]  # normalized width 0-1
+
+        if self.augment:
+            # flip up-down
+            if random.random() < hyp['flipud']:
+                img = np.flipud(img)
+                if nL:
+                    labels[:, 2] = 1 - labels[:, 2]
+
+            # flip left-right
+            if random.random() < hyp['fliplr']:
+                img = np.fliplr(img)
+                if nL:
+                    labels[:, 1] = 1 - labels[:, 1]
+
+        labels_out = torch.zeros((nL, 6))
+        if nL:
+            labels_out[:, 1:] = torch.from_numpy(labels)
+
+        # Convert
+        img = img[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416
+        img = np.ascontiguousarray(img)
+
+        return torch.from_numpy(img), labels_out, self.img_files[index], shapes
+
+    @staticmethod
+    def collate_fn(batch):
+        img, label, path, shapes = zip(*batch)  # transposed
+        for i, l in enumerate(label):
+            l[:, 0] = i  # add target image index for build_targets()
+        return torch.stack(img, 0), torch.cat(label, 0), path, shapes
+
+
+class LoadImagesAndLabels9(Dataset):  # for training/testing
+    def __init__(self, path, img_size=640, batch_size=16, augment=False, hyp=None, rect=False, image_weights=False,
+                 cache_images=False, single_cls=False, stride=32, pad=0.0, rank=-1):
+        self.img_size = img_size
+        self.augment = augment
+        self.hyp = hyp
+        self.image_weights = image_weights
+        self.rect = False if image_weights else rect
+        # load 4 images at a time into a mosaic (only during training)
+        self.mosaic = self.augment and not self.rect
+        self.mosaic_border = [-img_size // 2, -img_size // 2]
+        self.stride = stride
+
+        def img2label_paths(img_paths):
+            # Define label paths as a function of image paths
+            sa, sb = os.sep + 'images' + os.sep, os.sep + \
+                'labels' + os.sep  # /images/, /labels/ substrings
+            return [x.replace(sa, sb, 1).replace(x.split('.')[-1], 'txt') for x in img_paths]
+
+        try:
+            f = []  # image files
+            for p in path if isinstance(path, list) else [path]:
+                p = Path(p)  # os-agnostic
+                if p.is_dir():  # dir
+                    f += glob.glob(str(p / '**' / '*.*'), recursive=True)
+                elif p.is_file():  # file
+                    with open(p, 'r') as t:
+                        t = t.read().splitlines()
+                        parent = str(p.parent) + os.sep
+                        # local to global path
+                        f += [x.replace('./', parent)
+                              if x.startswith('./') else x for x in t]
+                else:
+                    raise Exception('%s does not exist' % p)
+            self.img_files = sorted(
+                [x.replace('/', os.sep) for x in f if x.split('.')[-1].lower() in img_formats])
+            assert self.img_files, 'No images found'
+        except Exception as e:
+            raise Exception('Error loading data from %s: %s\nSee %s' %
+                            (path, e, help_url))
+
+        # Check cache
+        self.label_files = img2label_paths(self.img_files)  # labels
+        # cached labels
+        cache_path = str(Path(self.label_files[0]).parent) + '.cache3'
+        if os.path.isfile(cache_path):
+            cache = torch.load(cache_path)  # load
+            # dataset changed
+            if cache['hash'] != get_hash(self.label_files + self.img_files):
+                cache = self.cache_labels(cache_path)  # re-cache
+        else:
+            cache = self.cache_labels(cache_path)  # cache
+
+        # Read cache
+        cache.pop('hash')  # remove hash
+        labels, shapes = zip(*cache.values())
+        self.labels = list(labels)
+        self.shapes = np.array(shapes, dtype=np.float64)
+        self.img_files = list(cache.keys())  # update
+        self.label_files = img2label_paths(cache.keys())  # update
+
+        n = len(shapes)  # number of images
+        bi = np.floor(np.arange(n) / batch_size).astype(np.int)  # batch index
+        nb = bi[-1] + 1  # number of batches
+        self.batch = bi  # batch index of image
+        self.n = n
+
+        # Rectangular Training
+        if self.rect:
+            # Sort by aspect ratio
+            s = self.shapes  # wh
+            ar = s[:, 1] / s[:, 0]  # aspect ratio
+            irect = ar.argsort()
+            self.img_files = [self.img_files[i] for i in irect]
+            self.label_files = [self.label_files[i] for i in irect]
+            self.labels = [self.labels[i] for i in irect]
+            self.shapes = s[irect]  # wh
+            ar = ar[irect]
+
+            # Set training image shapes
+            shapes = [[1, 1]] * nb
+            for i in range(nb):
+                ari = ar[bi == i]
+                mini, maxi = ari.min(), ari.max()
+                if maxi < 1:
+                    shapes[i] = [maxi, 1]
+                elif mini > 1:
+                    shapes[i] = [1, 1 / mini]
+
+            self.batch_shapes = np.ceil(
+                np.array(shapes) * img_size / stride + pad).astype(np.int) * stride
+
+        # Check labels
+        create_datasubset, extract_bounding_boxes, labels_loaded = False, False, False
+        # number missing, found, empty, datasubset, duplicate
+        nm, nf, ne, ns, nd = 0, 0, 0, 0, 0
+        pbar = enumerate(self.label_files)
+        if rank in [-1, 0]:
+            pbar = tqdm(pbar)
+        for i, file in pbar:
+            l = self.labels[i]  # label
+            if l is not None and l.shape[0]:
+                assert l.shape[1] == 5, '> 5 label columns: %s' % file
+                assert (l >= 0).all(), 'negative labels: %s' % file
+                assert (l[:, 1:] <= 1).all(
+                ), 'non-normalized or out of bounds coordinate labels: %s' % file
+                if np.unique(l, axis=0).shape[0] < l.shape[0]:  # duplicate rows
+                    # print('WARNING: duplicate rows in %s' % self.label_files[i])  # duplicate rows
+                    nd += 1
+                if single_cls:
+                    l[:, 0] = 0  # force dataset into single-class mode
+                self.labels[i] = l
+                nf += 1  # file found
+
+                # Create subdataset (a smaller dataset)
+                if create_datasubset and ns < 1E4:
+                    if ns == 0:
+                        create_folder(path='./datasubset')
+                        os.makedirs('./datasubset/images')
+                    exclude_classes = 43
+                    if exclude_classes not in l[:, 0]:
+                        ns += 1
+                        # shutil.copy(src=self.img_files[i], dst='./datasubset/images/')  # copy image
+                        with open('./datasubset/images.txt', 'a') as f:
+                            f.write(self.img_files[i] + '\n')
+
+                # Extract object detection boxes for a second stage classifier
+                if extract_bounding_boxes:
+                    p = Path(self.img_files[i])
+                    img = cv2.imread(str(p))
+                    h, w = img.shape[:2]
+                    for j, x in enumerate(l):
+                        f = '%s%sclassifier%s%g_%g_%s' % (
+                            p.parent.parent, os.sep, os.sep, x[0], j, p.name)
+                        if not os.path.exists(Path(f).parent):
+                            # make new output folder
+                            os.makedirs(Path(f).parent)
+
+                        b = x[1:] * [w, h, w, h]  # box
+                        b[2:] = b[2:].max()  # rectangle to square
+                        b[2:] = b[2:] * 1.3 + 30  # pad
+                        b = xywh2xyxy(b.reshape(-1, 4)).ravel().astype(np.int)
+
+                        # clip boxes outside of image
+                        b[[0, 2]] = np.clip(b[[0, 2]], 0, w)
+                        b[[1, 3]] = np.clip(b[[1, 3]], 0, h)
+                        assert cv2.imwrite(
+                            f, img[b[1]:b[3], b[0]:b[2]]), 'Failure extracting classifier boxes'
+            else:
+                # print('empty labels for image %s' % self.img_files[i])  # file empty
+                ne += 1
+                # os.system("rm '%s' '%s'" % (self.img_files[i], self.label_files[i]))  # remove
+
+            if rank in [-1, 0]:
+                pbar.desc = 'Scanning labels %s (%g found, %g missing, %g empty, %g duplicate, for %g images)' % (
+                    cache_path, nf, nm, ne, nd, n)
+        if nf == 0:
+            s = 'WARNING: No labels found in %s. See %s' % (
+                os.path.dirname(file) + os.sep, help_url)
+            print(s)
+            assert not augment, '%s. Can not train without labels.' % s
+
+        # Cache images into memory for faster training (WARNING: large datasets may exceed system RAM)
+        self.imgs = [None] * n
+        if cache_images:
+            gb = 0  # Gigabytes of cached images
+            self.img_hw0, self.img_hw = [None] * n, [None] * n
+            results = ThreadPool(8).imap(lambda x: load_image(
+                *x), zip(repeat(self), range(n)))  # 8 threads
+            pbar = tqdm(enumerate(results), total=n)
+            for i, x in pbar:
+                # img, hw_original, hw_resized = load_image(self, i)
+                self.imgs[i], self.img_hw0[i], self.img_hw[i] = x
+                gb += self.imgs[i].nbytes
+                pbar.desc = 'Caching images (%.1fGB)' % (gb / 1E9)
+
+    def cache_labels(self, path='labels.cache3'):
+        # Cache dataset labels, check images and read shapes
+        x = {}  # dict
+        pbar = tqdm(zip(self.img_files, self.label_files),
+                    desc='Scanning images', total=len(self.img_files))
+        for (img, label) in pbar:
+            try:
+                l = []
+                im = Image.open(img)
+                im.verify()  # PIL verify
+                shape = exif_size(im)  # image size
+                assert (shape[0] > 9) & (shape[1] > 9), 'image size <10 pixels'
+                if os.path.isfile(label):
+                    with open(label, 'r') as f:
+                        l = np.array(
+                            [x.split() for x in f.read().splitlines()], dtype=np.float32)  # labels
+                if len(l) == 0:
+                    l = np.zeros((0, 5), dtype=np.float32)
+                x[img] = [l, shape]
+            except Exception as e:
+                print('WARNING: Ignoring corrupted image and/or label %s: %s' % (img, e))
+
+        x['hash'] = get_hash(self.label_files + self.img_files)
+        torch.save(x, path)  # save for next time
+        return x
+
+    def __len__(self):
+        return len(self.img_files)
+
+    # def __iter__(self):
+    #     self.count = -1
+    #     print('ran dataset iter')
+    #     #self.shuffled_vector = np.random.permutation(self.nF) if self.augment else np.arange(self.nF)
+    #     return self
+
+    def __getitem__(self, index):
+        if self.image_weights:
+            index = self.indices[index]
+
+        hyp = self.hyp
+        mosaic = self.mosaic and random.random() < hyp['mosaic']
+        if mosaic:
+            # Load mosaic
+            # img, labels = load_mosaic(self, index)
+            img, labels = load_mosaic9(self, index)
+            shapes = None
+
+            # MixUp https://arxiv.org/pdf/1710.09412.pdf
+            if random.random() < hyp['mixup']:
+                # img2, labels2 = load_mosaic(self, random.randint(0, len(self.labels) - 1))
+                img2, labels2 = load_mosaic9(
+                    self, random.randint(0, len(self.labels) - 1))
+                r = np.random.beta(8.0, 8.0)  # mixup ratio, alpha=beta=8.0
+                img = (img * r + img2 * (1 - r)).astype(np.uint8)
+                labels = np.concatenate((labels, labels2), 0)
+
+        else:
+            # Load image
+            img, (h0, w0), (h, w) = load_image(self, index)
+
+            # Letterbox
+            # final letterboxed shape
+            shape = self.batch_shapes[self.batch[index]
+                                      ] if self.rect else self.img_size
+            img, ratio, pad = letterbox(
+                img, shape, auto=False, scaleup=self.augment)
+            # for COCO mAP rescaling
+            shapes = (h0, w0), ((h / h0, w / w0), pad)
+
+            # Load labels
+            labels = []
+            x = self.labels[index]
+            if x.size > 0:
+                # Normalized xywh to pixel xyxy format
+                labels = x.copy()
+                labels[:, 1] = ratio[0] * w * \
+                    (x[:, 1] - x[:, 3] / 2) + pad[0]  # pad width
+                labels[:, 2] = ratio[1] * h * \
+                    (x[:, 2] - x[:, 4] / 2) + pad[1]  # pad height
+                labels[:, 3] = ratio[0] * w * (x[:, 1] + x[:, 3] / 2) + pad[0]
+                labels[:, 4] = ratio[1] * h * (x[:, 2] + x[:, 4] / 2) + pad[1]
+
+        if self.augment:
+            # Augment imagespace
+            if not mosaic:
+                img, labels = random_perspective(img, labels,
+                                                 degrees=hyp['degrees'],
+                                                 translate=hyp['translate'],
+                                                 scale=hyp['scale'],
+                                                 shear=hyp['shear'],
+                                                 perspective=hyp['perspective'])
+
+            # Augment colorspace
+            augment_hsv(img, hgain=hyp['hsv_h'],
+                        sgain=hyp['hsv_s'], vgain=hyp['hsv_v'])
+
+            # Apply cutouts
+            # if random.random() < 0.9:
+            #     labels = cutout(img, labels)
+
+        nL = len(labels)  # number of labels
+        if nL:
+            labels[:, 1:5] = xyxy2xywh(labels[:, 1:5])  # convert xyxy to xywh
+            labels[:, [2, 4]] /= img.shape[0]  # normalized height 0-1
+            labels[:, [1, 3]] /= img.shape[1]  # normalized width 0-1
+
+        if self.augment:
+            # flip up-down
+            if random.random() < hyp['flipud']:
+                img = np.flipud(img)
+                if nL:
+                    labels[:, 2] = 1 - labels[:, 2]
+
+            # flip left-right
+            if random.random() < hyp['fliplr']:
+                img = np.fliplr(img)
+                if nL:
+                    labels[:, 1] = 1 - labels[:, 1]
+
+        labels_out = torch.zeros((nL, 6))
+        if nL:
+            labels_out[:, 1:] = torch.from_numpy(labels)
+
+        # Convert
+        img = img[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416
+        img = np.ascontiguousarray(img)
+
+        return torch.from_numpy(img), labels_out, self.img_files[index], shapes
+
+    @staticmethod
+    def collate_fn(batch):
+        img, label, path, shapes = zip(*batch)  # transposed
+        for i, l in enumerate(label):
+            l[:, 0] = i  # add target image index for build_targets()
+        return torch.stack(img, 0), torch.cat(label, 0), path, shapes
+
+
+# Ancillary functions --------------------------------------------------------------------------------------------------
+def load_image(self, index):
+    # loads 1 image from dataset, returns img, original hw, resized hw
+    img = self.imgs[index]
+    if img is None:  # not cached
+        path = self.img_files[index]
+        img = cv2.imread(path)  # BGR
+        assert img is not None, 'Image Not Found ' + path
+        h0, w0 = img.shape[:2]  # orig hw
+        r = self.img_size / max(h0, w0)  # resize image to img_size
+        if r != 1:  # always resize down, only resize up if training with augmentation
+            interp = cv2.INTER_AREA if r < 1 and not self.augment else cv2.INTER_LINEAR
+            img = cv2.resize(img, (int(w0 * r), int(h0 * r)),
+                             interpolation=interp)
+        return img, (h0, w0), img.shape[:2]  # img, hw_original, hw_resized
+    else:
+        # img, hw_original, hw_resized
+        return self.imgs[index], self.img_hw0[index], self.img_hw[index]
+
+
+def augment_hsv(img, hgain=0.5, sgain=0.5, vgain=0.5):
+    r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1  # random gains
+    hue, sat, val = cv2.split(cv2.cvtColor(img, cv2.COLOR_BGR2HSV))
+    dtype = img.dtype  # uint8
+
+    x = np.arange(0, 256, dtype=np.int16)
+    lut_hue = ((x * r[0]) % 180).astype(dtype)
+    lut_sat = np.clip(x * r[1], 0, 255).astype(dtype)
+    lut_val = np.clip(x * r[2], 0, 255).astype(dtype)
+
+    img_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(
+        sat, lut_sat), cv2.LUT(val, lut_val))).astype(dtype)
+    cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img)  # no return needed
+
+    # Histogram equalization
+    # if random.random() < 0.2:
+    #     for i in range(3):
+    #         img[:, :, i] = cv2.equalizeHist(img[:, :, i])
+
+
+def load_mosaic(self, index):
+    # loads images in a mosaic
+
+    labels4 = []
+    s = self.img_size
+    yc, xc = [int(random.uniform(-x, 2 * s + x))
+              for x in self.mosaic_border]  # mosaic center x, y
+    # 3 additional image indices
+    indices = [index] + \
+        [random.randint(0, len(self.labels) - 1) for _ in range(3)]
+    for i, index in enumerate(indices):
+        # Load image
+        img, _, (h, w) = load_image(self, index)
+
+        # place img in img4
+        if i == 0:  # top left
+            # base image with 4 tiles
+            img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8)
+            # xmin, ymin, xmax, ymax (large image)
+            x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc
+            # xmin, ymin, xmax, ymax (small image)
+            x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h
+        elif i == 1:  # top right
+            x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc
+            x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h
+        elif i == 2:  # bottom left
+            x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h)
+            x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h)
+        elif i == 3:  # bottom right
+            x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h)
+            x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h)
+
+        # img4[ymin:ymax, xmin:xmax]
+        img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b]
+        padw = x1a - x1b
+        padh = y1a - y1b
+
+        # Labels
+        x = self.labels[index]
+        labels = x.copy()
+        if x.size > 0:  # Normalized xywh to pixel xyxy format
+            labels[:, 1] = w * (x[:, 1] - x[:, 3] / 2) + padw
+            labels[:, 2] = h * (x[:, 2] - x[:, 4] / 2) + padh
+            labels[:, 3] = w * (x[:, 1] + x[:, 3] / 2) + padw
+            labels[:, 4] = h * (x[:, 2] + x[:, 4] / 2) + padh
+        labels4.append(labels)
+
+    # Concat/clip labels
+    if len(labels4):
+        labels4 = np.concatenate(labels4, 0)
+        # use with random_perspective
+        np.clip(labels4[:, 1:], 0, 2 * s, out=labels4[:, 1:])
+        # img4, labels4 = replicate(img4, labels4)  # replicate
+
+    # Augment
+    img4, labels4 = random_perspective(img4, labels4,
+                                       degrees=self.hyp['degrees'],
+                                       translate=self.hyp['translate'],
+                                       scale=self.hyp['scale'],
+                                       shear=self.hyp['shear'],
+                                       perspective=self.hyp['perspective'],
+                                       border=self.mosaic_border)  # border to remove
+
+    return img4, labels4
+
+
+def load_mosaic9(self, index):
+    # loads images in a 9-mosaic
+
+    labels9 = []
+    s = self.img_size
+    # 8 additional image indices
+    indices = [index] + \
+        [random.randint(0, len(self.labels) - 1) for _ in range(8)]
+    for i, index in enumerate(indices):
+        # Load image
+        img, _, (h, w) = load_image(self, index)
+
+        # place img in img9
+        if i == 0:  # center
+            # base image with 4 tiles
+            img9 = np.full((s * 3, s * 3, img.shape[2]), 114, dtype=np.uint8)
+            h0, w0 = h, w
+            c = s, s, s + w, s + h  # xmin, ymin, xmax, ymax (base) coordinates
+        elif i == 1:  # top
+            c = s, s - h, s + w, s
+        elif i == 2:  # top right
+            c = s + wp, s - h, s + wp + w, s
+        elif i == 3:  # right
+            c = s + w0, s, s + w0 + w, s + h
+        elif i == 4:  # bottom right
+            c = s + w0, s + hp, s + w0 + w, s + hp + h
+        elif i == 5:  # bottom
+            c = s + w0 - w, s + h0, s + w0, s + h0 + h
+        elif i == 6:  # bottom left
+            c = s + w0 - wp - w, s + h0, s + w0 - wp, s + h0 + h
+        elif i == 7:  # left
+            c = s - w, s + h0 - h, s, s + h0
+        elif i == 8:  # top left
+            c = s - w, s + h0 - hp - h, s, s + h0 - hp
+
+        padx, pady = c[:2]
+        x1, y1, x2, y2 = [max(x, 0) for x in c]  # allocate coords
+
+        # Labels
+        x = self.labels[index]
+        labels = x.copy()
+        if x.size > 0:  # Normalized xywh to pixel xyxy format
+            labels[:, 1] = w * (x[:, 1] - x[:, 3] / 2) + padx
+            labels[:, 2] = h * (x[:, 2] - x[:, 4] / 2) + pady
+            labels[:, 3] = w * (x[:, 1] + x[:, 3] / 2) + padx
+            labels[:, 4] = h * (x[:, 2] + x[:, 4] / 2) + pady
+        labels9.append(labels)
+
+        # Image
+        # img9[ymin:ymax, xmin:xmax]
+        img9[y1:y2, x1:x2] = img[y1 - pady:, x1 - padx:]
+        hp, wp = h, w  # height, width previous
+
+    # Offset
+    yc, xc = [int(random.uniform(0, s))
+              for x in self.mosaic_border]  # mosaic center x, y
+    img9 = img9[yc:yc + 2 * s, xc:xc + 2 * s]
+
+    # Concat/clip labels
+    if len(labels9):
+        labels9 = np.concatenate(labels9, 0)
+        labels9[:, [1, 3]] -= xc
+        labels9[:, [2, 4]] -= yc
+
+        # use with random_perspective
+        np.clip(labels9[:, 1:], 0, 2 * s, out=labels9[:, 1:])
+        # img9, labels9 = replicate(img9, labels9)  # replicate
+
+    # Augment
+    img9, labels9 = random_perspective(img9, labels9,
+                                       degrees=self.hyp['degrees'],
+                                       translate=self.hyp['translate'],
+                                       scale=self.hyp['scale'],
+                                       shear=self.hyp['shear'],
+                                       perspective=self.hyp['perspective'],
+                                       border=self.mosaic_border)  # border to remove
+
+    return img9, labels9
+
+
+def replicate(img, labels):
+    # Replicate labels
+    h, w = img.shape[:2]
+    boxes = labels[:, 1:].astype(int)
+    x1, y1, x2, y2 = boxes.T
+    s = ((x2 - x1) + (y2 - y1)) / 2  # side length (pixels)
+    for i in s.argsort()[:round(s.size * 0.5)]:  # smallest indices
+        x1b, y1b, x2b, y2b = boxes[i]
+        bh, bw = y2b - y1b, x2b - x1b
+        yc, xc = int(random.uniform(0, h - bh)
+                     ), int(random.uniform(0, w - bw))  # offset x, y
+        x1a, y1a, x2a, y2a = [xc, yc, xc + bw, yc + bh]
+        # img4[ymin:ymax, xmin:xmax]
+        img[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b]
+        labels = np.append(
+            labels, [[labels[i, 0], x1a, y1a, x2a, y2a]], axis=0)
+
+    return img, labels
+
+
+def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, auto_size=32):
+    # Resize image to a 32-pixel-multiple rectangle https://github.com/ultralytics/yolov3/issues/232
+    shape = img.shape[:2]  # current shape [height, width]
+    if isinstance(new_shape, int):
+        new_shape = (new_shape, new_shape)
+
+    # Scale ratio (new / old)
+    r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
+    if not scaleup:  # only scale down, do not scale up (for better test mAP)
+        r = min(r, 1.0)
+
+    # Compute padding
+    ratio = r, r  # width, height ratios
+    new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
+    dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - \
+        new_unpad[1]  # wh padding
+    if auto:  # minimum rectangle
+        dw, dh = np.mod(dw, auto_size), np.mod(dh, auto_size)  # wh padding
+    elif scaleFill:  # stretch
+        dw, dh = 0.0, 0.0
+        new_unpad = (new_shape[1], new_shape[0])
+        ratio = new_shape[1] / shape[1], new_shape[0] / \
+            shape[0]  # width, height ratios
+
+    dw /= 2  # divide padding into 2 sides
+    dh /= 2
+
+    if shape[::-1] != new_unpad:  # resize
+        img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
+    top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
+    left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
+    img = cv2.copyMakeBorder(
+        img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add border
+    return img, ratio, (dw, dh)
+
+
+def random_perspective(img, targets=(), degrees=10, translate=.1, scale=.1, shear=10, perspective=0.0, border=(0, 0)):
+    # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10))
+    # targets = [cls, xyxy]
+
+    height = img.shape[0] + border[0] * 2  # shape(h,w,c)
+    width = img.shape[1] + border[1] * 2
+
+    # Center
+    C = np.eye(3)
+    C[0, 2] = -img.shape[1] / 2  # x translation (pixels)
+    C[1, 2] = -img.shape[0] / 2  # y translation (pixels)
+
+    # Perspective
+    P = np.eye(3)
+    # x perspective (about y)
+    P[2, 0] = random.uniform(-perspective, perspective)
+    # y perspective (about x)
+    P[2, 1] = random.uniform(-perspective, perspective)
+
+    # Rotation and Scale
+    R = np.eye(3)
+    a = random.uniform(-degrees, degrees)
+    # a += random.choice([-180, -90, 0, 90])  # add 90deg rotations to small rotations
+    s = random.uniform(1 - scale, 1 + scale)
+    # s = 2 ** random.uniform(-scale, scale)
+    R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s)
+
+    # Shear
+    S = np.eye(3)
+    S[0, 1] = math.tan(random.uniform(-shear, shear) *
+                       math.pi / 180)  # x shear (deg)
+    S[1, 0] = math.tan(random.uniform(-shear, shear) *
+                       math.pi / 180)  # y shear (deg)
+
+    # Translation
+    T = np.eye(3)
+    T[0, 2] = random.uniform(0.5 - translate, 0.5 +
+                             translate) * width  # x translation (pixels)
+    T[1, 2] = random.uniform(0.5 - translate, 0.5 +
+                             translate) * height  # y translation (pixels)
+
+    # Combined rotation matrix
+    M = T @ S @ R @ P @ C  # order of operations (right to left) is IMPORTANT
+    if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any():  # image changed
+        if perspective:
+            img = cv2.warpPerspective(img, M, dsize=(
+                width, height), borderValue=(114, 114, 114))
+        else:  # affine
+            img = cv2.warpAffine(img, M[:2], dsize=(
+                width, height), borderValue=(114, 114, 114))
+
+    # Visualize
+    # import matplotlib.pyplot as plt
+    # ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel()
+    # ax[0].imshow(img[:, :, ::-1])  # base
+    # ax[1].imshow(img2[:, :, ::-1])  # warped
+
+    # Transform label coordinates
+    n = len(targets)
+    if n:
+        # warp points
+        xy = np.ones((n * 4, 3))
+        xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(
+            n * 4, 2)  # x1y1, x2y2, x1y2, x2y1
+        xy = xy @ M.T  # transform
+        if perspective:
+            xy = (xy[:, :2] / xy[:, 2:3]).reshape(n, 8)  # rescale
+        else:  # affine
+            xy = xy[:, :2].reshape(n, 8)
+
+        # create new boxes
+        x = xy[:, [0, 2, 4, 6]]
+        y = xy[:, [1, 3, 5, 7]]
+        xy = np.concatenate(
+            (x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T
+
+        # # apply angle-based reduction of bounding boxes
+        # radians = a * math.pi / 180
+        # reduction = max(abs(math.sin(radians)), abs(math.cos(radians))) ** 0.5
+        # x = (xy[:, 2] + xy[:, 0]) / 2
+        # y = (xy[:, 3] + xy[:, 1]) / 2
+        # w = (xy[:, 2] - xy[:, 0]) * reduction
+        # h = (xy[:, 3] - xy[:, 1]) * reduction
+        # xy = np.concatenate((x - w / 2, y - h / 2, x + w / 2, y + h / 2)).reshape(4, n).T
+
+        # clip boxes
+        xy[:, [0, 2]] = xy[:, [0, 2]].clip(0, width)
+        xy[:, [1, 3]] = xy[:, [1, 3]].clip(0, height)
+
+        # filter candidates
+        i = box_candidates(box1=targets[:, 1:5].T * s, box2=xy.T)
+        targets = targets[i]
+        targets[:, 1:5] = xy[i]
+
+    return img, targets
+
+
+def box_candidates(box1, box2, wh_thr=2, ar_thr=20, area_thr=0.1):  # box1(4,n), box2(4,n)
+    # Compute candidate boxes: box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio
+    w1, h1 = box1[2] - box1[0], box1[3] - box1[1]
+    w2, h2 = box2[2] - box2[0], box2[3] - box2[1]
+    ar = np.maximum(w2 / (h2 + 1e-16), h2 / (w2 + 1e-16))  # aspect ratio
+    # candidates
+    return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + 1e-16) > area_thr) & (ar < ar_thr)
+
+
+def cutout(image, labels):
+    # Applies image cutout augmentation https://arxiv.org/abs/1708.04552
+    h, w = image.shape[:2]
+
+    def bbox_ioa(box1, box2):
+        # Returns the intersection over box2 area given box1, box2. box1 is 4, box2 is nx4. boxes are x1y1x2y2
+        box2 = box2.transpose()
+
+        # Get the coordinates of bounding boxes
+        b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3]
+        b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3]
+
+        # Intersection area
+        inter_area = (np.minimum(b1_x2, b2_x2) - np.maximum(b1_x1, b2_x1)).clip(0) * \
+                     (np.minimum(b1_y2, b2_y2) - np.maximum(b1_y1, b2_y1)).clip(0)
+
+        # box2 area
+        box2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1) + 1e-16
+
+        # Intersection over box2 area
+        return inter_area / box2_area
+
+    # create random masks
+    scales = [0.5] * 1 + [0.25] * 2 + [0.125] * 4 + \
+        [0.0625] * 8 + [0.03125] * 16  # image size fraction
+    for s in scales:
+        mask_h = random.randint(1, int(h * s))
+        mask_w = random.randint(1, int(w * s))
+
+        # box
+        xmin = max(0, random.randint(0, w) - mask_w // 2)
+        ymin = max(0, random.randint(0, h) - mask_h // 2)
+        xmax = min(w, xmin + mask_w)
+        ymax = min(h, ymin + mask_h)
+
+        # apply random color mask
+        image[ymin:ymax, xmin:xmax] = [
+            random.randint(64, 191) for _ in range(3)]
+
+        # return unobscured labels
+        if len(labels) and s > 0.03:
+            box = np.array([xmin, ymin, xmax, ymax], dtype=np.float32)
+            ioa = bbox_ioa(box, labels[:, 1:5])  # intersection over area
+            labels = labels[ioa < 0.60]  # remove >60% obscured labels
+
+    return labels
+
+
+def create_folder(path='./new'):
+    # Create folder
+    if os.path.exists(path):
+        shutil.rmtree(path)  # delete output folder
+    os.makedirs(path)  # make new output folder
+
+
+def flatten_recursive(path='../coco128'):
+    # Flatten a recursive directory by bringing all files to top level
+    new_path = Path(path + '_flat')
+    create_folder(new_path)
+    for file in tqdm(glob.glob(str(Path(path)) + '/**/*.*', recursive=True)):
+        shutil.copyfile(file, new_path / Path(file).name)
diff --git a/model_image/yolor/utils/general.py b/model_image/yolor/utils/general.py
new file mode 100644
index 0000000..cd5eefd
--- /dev/null
+++ b/model_image/yolor/utils/general.py
@@ -0,0 +1,480 @@
+# General utils
+
+import glob
+import logging
+import math
+import os
+import platform
+import random
+import re
+import subprocess
+import time
+from pathlib import Path
+
+import cv2
+import numpy as np
+import torch
+import yaml
+
+from yolor.utils.google_utils import gsutil_getsize
+from yolor.utils.metrics import fitness
+from yolor.utils.torch_utils import init_torch_seeds
+
+# Set printoptions
+torch.set_printoptions(linewidth=320, precision=5, profile='long')
+# format short g, %precision=5
+np.set_printoptions(linewidth=320, formatter={'float_kind': '{:11.5g}'.format})
+
+# Prevent OpenCV from multithreading (to use PyTorch DataLoader)
+cv2.setNumThreads(0)
+
+
+def set_logging(rank=-1):
+    logging.basicConfig(
+        format="%(message)s",
+        level=logging.INFO if rank in [-1, 0] else logging.WARN)
+
+
+def init_seeds(seed=0):
+    random.seed(seed)
+    np.random.seed(seed)
+    init_torch_seeds(seed)
+
+
+def get_latest_run(search_dir='.'):
+    # Return path to most recent 'last.pt' in /runs (i.e. to --resume from)
+    last_list = glob.glob(f'{search_dir}/**/last*.pt', recursive=True)
+    return max(last_list, key=os.path.getctime) if last_list else ''
+
+
+def check_git_status():
+    # Suggest 'git pull' if repo is out of date
+    if platform.system() in ['Linux', 'Darwin'] and not os.path.isfile('/.dockerenv'):
+        s = subprocess.check_output(
+            'if [ -d .git ]; then git fetch && git status -uno; fi', shell=True).decode('utf-8')
+        if 'Your branch is behind' in s:
+            print(s[s.find('Your branch is behind'):s.find('\n\n')] + '\n')
+
+
+def check_img_size(img_size, s=32):
+    # Verify img_size is a multiple of stride s
+    new_size = make_divisible(img_size, int(s))  # ceil gs-multiple
+    if new_size != img_size:
+        print('WARNING: --img-size %g must be multiple of max stride %g, updating to %g' %
+              (img_size, s, new_size))
+    return new_size
+
+
+def check_file(file):
+    # Search for file if not found
+    if os.path.isfile(file) or file == '':
+        return file
+    else:
+        files = glob.glob('./**/' + file, recursive=True)  # find file
+        assert len(files), 'File Not Found: %s' % file  # assert file was found
+        assert len(files) == 1, "Multiple files match '%s', specify exact path: %s" % (
+            file, files)  # assert unique
+        return files[0]  # return file
+
+
+def check_dataset(dict):
+    # Download dataset if not found locally
+    val, s = dict.get('val'), dict.get('download')
+    if val and len(val):
+        val = [Path(x).resolve()
+               for x in (val if isinstance(val, list) else [val])]  # val path
+        if not all(x.exists() for x in val):
+            print('\nWARNING: Dataset not found, nonexistent paths: %s' %
+                  [str(x) for x in val if not x.exists()])
+            if s and len(s):  # download script
+                print('Downloading %s ...' % s)
+                if s.startswith('http') and s.endswith('.zip'):  # URL
+                    f = Path(s).name  # filename
+                    torch.hub.download_url_to_file(s, f)
+                    r = os.system('unzip -q %s -d ../ && rm %s' %
+                                  (f, f))  # unzip
+                else:  # bash script
+                    r = os.system(s)
+                print('Dataset autodownload %s\n' % ('success' if r ==
+                      0 else 'failure'))  # analyze return value
+            else:
+                raise Exception('Dataset not found.')
+
+
+def make_divisible(x, divisor):
+    # Returns x evenly divisible by divisor
+    return math.ceil(x / divisor) * divisor
+
+
+def labels_to_class_weights(labels, nc=80):
+    # Get class weights (inverse frequency) from training labels
+    if labels[0] is None:  # no labels loaded
+        return torch.Tensor()
+
+    labels = np.concatenate(labels, 0)  # labels.shape = (866643, 5) for COCO
+    classes = labels[:, 0].astype(np.int)  # labels = [class xywh]
+    weights = np.bincount(classes, minlength=nc)  # occurrences per class
+
+    # Prepend gridpoint count (for uCE training)
+    # gpi = ((320 / 32 * np.array([1, 2, 4])) ** 2 * 3).sum()  # gridpoints per image
+    # weights = np.hstack([gpi * len(labels)  - weights.sum() * 9, weights * 9]) ** 0.5  # prepend gridpoints to start
+
+    weights[weights == 0] = 1  # replace empty bins with 1
+    weights = 1 / weights  # number of targets per class
+    weights /= weights.sum()  # normalize
+    return torch.from_numpy(weights)
+
+
+def labels_to_image_weights(labels, nc=80, class_weights=np.ones(80)):
+    # Produces image weights based on class mAPs
+    n = len(labels)
+    class_counts = np.array(
+        [np.bincount(labels[i][:, 0].astype(np.int), minlength=nc) for i in range(n)])
+    image_weights = (class_weights.reshape(1, nc) * class_counts).sum(1)
+    # index = random.choices(range(n), weights=image_weights, k=1)  # weight image sample
+    return image_weights
+
+
+def coco80_to_coco91_class():  # converts 80-index (val2014) to 91-index (paper)
+    # https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/
+    # a = np.loadtxt('data/coco.names', dtype='str', delimiter='\n')
+    # b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter='\n')
+    # x1 = [list(a[i] == b).index(True) + 1 for i in range(80)]  # darknet to coco
+    # x2 = [list(b[i] == a).index(True) if any(b[i] == a) else None for i in range(91)]  # coco to darknet
+    x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34,
+         35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
+         64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90]
+    return x
+
+
+def xyxy2xywh(x):
+    # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right
+    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
+    y[:, 0] = (x[:, 0] + x[:, 2]) / 2  # x center
+    y[:, 1] = (x[:, 1] + x[:, 3]) / 2  # y center
+    y[:, 2] = x[:, 2] - x[:, 0]  # width
+    y[:, 3] = x[:, 3] - x[:, 1]  # height
+    return y
+
+
+def xywh2xyxy(x):
+    # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
+    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
+    y[:, 0] = x[:, 0] - x[:, 2] / 2  # top left x
+    y[:, 1] = x[:, 1] - x[:, 3] / 2  # top left y
+    y[:, 2] = x[:, 0] + x[:, 2] / 2  # bottom right x
+    y[:, 3] = x[:, 1] + x[:, 3] / 2  # bottom right y
+    return y
+
+
+def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None):
+    # Rescale coords (xyxy) from img1_shape to img0_shape
+    if ratio_pad is None:  # calculate from img0_shape
+        gain = min(img1_shape[0] / img0_shape[0],
+                   img1_shape[1] / img0_shape[1])  # gain  = old / new
+        pad = (img1_shape[1] - img0_shape[1] * gain) / \
+            2, (img1_shape[0] - img0_shape[0] * gain) / 2  # wh padding
+    else:
+        gain = ratio_pad[0][0]
+        pad = ratio_pad[1]
+
+    coords[:, [0, 2]] -= pad[0]  # x padding
+    coords[:, [1, 3]] -= pad[1]  # y padding
+    coords[:, :4] /= gain
+    clip_coords(coords, img0_shape)
+    return coords
+
+
+def clip_coords(boxes, img_shape):
+    # Clip bounding xyxy bounding boxes to image shape (height, width)
+    boxes[:, 0].clamp_(0, img_shape[1])  # x1
+    boxes[:, 1].clamp_(0, img_shape[0])  # y1
+    boxes[:, 2].clamp_(0, img_shape[1])  # x2
+    boxes[:, 3].clamp_(0, img_shape[0])  # y2
+
+
+def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False, EIoU=False, ECIoU=False, eps=1e-9):
+    # Returns the IoU of box1 to box2. box1 is 4, box2 is nx4
+    box2 = box2.T
+
+    # Get the coordinates of bounding boxes
+    if x1y1x2y2:  # x1, y1, x2, y2 = box1
+        b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3]
+        b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3]
+    else:  # transform from xywh to xyxy
+        b1_x1, b1_x2 = box1[0] - box1[2] / 2, box1[0] + box1[2] / 2
+        b1_y1, b1_y2 = box1[1] - box1[3] / 2, box1[1] + box1[3] / 2
+        b2_x1, b2_x2 = box2[0] - box2[2] / 2, box2[0] + box2[2] / 2
+        b2_y1, b2_y2 = box2[1] - box2[3] / 2, box2[1] + box2[3] / 2
+
+    # Intersection area
+    inter = (torch.min(b1_x2, b2_x2) - torch.max(b1_x1, b2_x1)).clamp(0) * \
+            (torch.min(b1_y2, b2_y2) - torch.max(b1_y1, b2_y1)).clamp(0)
+
+    # Union Area
+    w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps
+    w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps
+    union = w1 * h1 + w2 * h2 - inter + eps
+
+    iou = inter / union
+    if GIoU or DIoU or CIoU or EIoU or ECIoU:
+        # convex (smallest enclosing box) width
+        cw = torch.max(b1_x2, b2_x2) - torch.min(b1_x1, b2_x1)
+        ch = torch.max(b1_y2, b2_y2) - torch.min(b1_y1, b2_y1)  # convex height
+        if CIoU or DIoU or EIoU or ECIoU:  # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1
+            c2 = cw ** 2 + ch ** 2 + eps  # convex diagonal squared
+            rho2 = ((b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 +
+                    (b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2) / 4  # center distance squared
+            if DIoU:
+                return iou - rho2 / c2  # DIoU
+            elif CIoU:  # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47
+                v = (4 / math.pi ** 2) * \
+                    torch.pow(torch.atan(w2 / h2) - torch.atan(w1 / h1), 2)
+                with torch.no_grad():
+                    alpha = v / ((1 + eps) - iou + v)
+                return iou - (rho2 / c2 + v * alpha)  # CIoU
+            elif EIoU:  # Efficient IoU https://arxiv.org/abs/2101.08158
+                rho3 = (w1-w2) ** 2
+                c3 = cw ** 2 + eps
+                rho4 = (h1-h2) ** 2
+                c4 = ch ** 2 + eps
+                return iou - rho2 / c2 - rho3 / c3 - rho4 / c4  # EIoU
+            elif ECIoU:
+                v = (4 / math.pi ** 2) * \
+                    torch.pow(torch.atan(w2 / h2) - torch.atan(w1 / h1), 2)
+                with torch.no_grad():
+                    alpha = v / ((1 + eps) - iou + v)
+                rho3 = (w1-w2) ** 2
+                c3 = cw ** 2 + eps
+                rho4 = (h1-h2) ** 2
+                c4 = ch ** 2 + eps
+                return iou - v * alpha - rho2 / c2 - rho3 / c3 - rho4 / c4  # ECIoU
+        else:  # GIoU https://arxiv.org/pdf/1902.09630.pdf
+            c_area = cw * ch + eps  # convex area
+            return iou - (c_area - union) / c_area  # GIoU
+    else:
+        return iou  # IoU
+
+
+def box_iou(box1, box2):
+    # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py
+    """
+    Return intersection-over-union (Jaccard index) of boxes.
+    Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
+    Arguments:
+        box1 (Tensor[N, 4])
+        box2 (Tensor[M, 4])
+    Returns:
+        iou (Tensor[N, M]): the NxM matrix containing the pairwise
+            IoU values for every element in boxes1 and boxes2
+    """
+
+    def box_area(box):
+        # box = 4xn
+        return (box[2] - box[0]) * (box[3] - box[1])
+
+    area1 = box_area(box1.T)
+    area2 = box_area(box2.T)
+
+    # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)
+    inter = (torch.min(box1[:, None, 2:], box2[:, 2:]) -
+             torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2)
+    # iou = inter / (area1 + area2 - inter)
+    return inter / (area1[:, None] + area2 - inter)
+
+
+def wh_iou(wh1, wh2):
+    # Returns the nxm IoU matrix. wh1 is nx2, wh2 is mx2
+    wh1 = wh1[:, None]  # [N,1,2]
+    wh2 = wh2[None]  # [1,M,2]
+    inter = torch.min(wh1, wh2).prod(2)  # [N,M]
+    # iou = inter / (area1 + area2 - inter)
+    return inter / (wh1.prod(2) + wh2.prod(2) - inter)
+
+
+def non_max_suppression(prediction, conf_thres=0.1, iou_thres=0.6, merge=False, classes=None, agnostic=False):
+    """Performs Non-Maximum Suppression (NMS) on inference results
+
+    Returns:
+         detections with shape: nx6 (x1, y1, x2, y2, conf, cls)
+    """
+
+    nc = prediction[0].shape[1] - 5  # number of classes
+    xc = prediction[..., 4] > conf_thres  # candidates
+
+    # Settings
+    # (pixels) minimum and maximum box width and height
+    min_wh, max_wh = 2, 4096
+    max_det = 300  # maximum number of detections per image
+    time_limit = 10.0  # seconds to quit after
+    redundant = True  # require redundant detections
+    multi_label = nc > 1  # multiple labels per box (adds 0.5ms/img)
+
+    t = time.time()
+    output = [torch.zeros(0, 6)] * prediction.shape[0]
+    for xi, x in enumerate(prediction):  # image index, image inference
+        # Apply constraints
+        # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0  # width-height
+        x = x[xc[xi]]  # confidence
+
+        # If none remain process next image
+        if not x.shape[0]:
+            continue
+
+        # Compute conf
+        x[:, 5:] *= x[:, 4:5]  # conf = obj_conf * cls_conf
+
+        # Box (center x, center y, width, height) to (x1, y1, x2, y2)
+        box = xywh2xyxy(x[:, :4])
+
+        # Detections matrix nx6 (xyxy, conf, cls)
+        if multi_label:
+            i, j = (x[:, 5:] > conf_thres).nonzero(as_tuple=False).T
+            x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1)
+        else:  # best class only
+            conf, j = x[:, 5:].max(1, keepdim=True)
+            x = torch.cat((box, conf, j.float()), 1)[
+                conf.view(-1) > conf_thres]
+
+        # Filter by class
+        if classes:
+            x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]
+
+        # Apply finite constraint
+        # if not torch.isfinite(x).all():
+        #     x = x[torch.isfinite(x).all(1)]
+
+        # If none remain process next image
+        n = x.shape[0]  # number of boxes
+        if not n:
+            continue
+
+        # Sort by confidence
+        # x = x[x[:, 4].argsort(descending=True)]
+
+        # Batched NMS
+        c = x[:, 5:6] * (0 if agnostic else max_wh)  # classes
+        # boxes (offset by class), scores
+        boxes, scores = x[:, :4] + c, x[:, 4]
+        i = torch.ops.torchvision.nms(boxes, scores, iou_thres)
+        if i.shape[0] > max_det:  # limit detections
+            i = i[:max_det]
+        if merge and (1 < n < 3E3):  # Merge NMS (boxes merged using weighted mean)
+            # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
+            iou = box_iou(boxes[i], boxes) > iou_thres  # iou matrix
+            weights = iou * scores[None]  # box weights
+            x[i, :4] = torch.mm(weights, x[:, :4]).float(
+            ) / weights.sum(1, keepdim=True)  # merged boxes
+            if redundant:
+                i = i[iou.sum(1) > 1]  # require redundancy
+
+        output[xi] = x[i]
+        if (time.time() - t) > time_limit:
+            break  # time limit exceeded
+
+    return output
+
+
+# from utils.general import *; strip_optimizer()
+def strip_optimizer(f='weights/best.pt', s=''):
+    # Strip optimizer from 'f' to finalize training, optionally save as 's'
+    x = torch.load(f, map_location=torch.device('cpu'))
+    x['optimizer'] = None
+    x['training_results'] = None
+    x['epoch'] = -1
+    # x['model'].half()  # to FP16
+    # for p in x['model'].parameters():
+    #    p.requires_grad = False
+    torch.save(x, s or f)
+    mb = os.path.getsize(s or f) / 1E6  # filesize
+    print('Optimizer stripped from %s,%s %.1fMB' %
+          (f, (' saved as %s,' % s) if s else '', mb))
+
+
+def print_mutation(hyp, results, yaml_file='hyp_evolved.yaml', bucket=''):
+    # Print mutation results to evolve.txt (for use with train.py --evolve)
+    a = '%10s' * len(hyp) % tuple(hyp.keys())  # hyperparam keys
+    b = '%10.3g' * len(hyp) % tuple(hyp.values())  # hyperparam values
+    # results (P, R, mAP@0.5, mAP@0.5:0.95, val_losses x 3)
+    c = '%10.4g' * len(results) % results
+    print('\n%s\n%s\nEvolved fitness: %s\n' % (a, b, c))
+
+    if bucket:
+        url = 'gs://%s/evolve.txt' % bucket
+        if gsutil_getsize(url) > (os.path.getsize('evolve.txt') if os.path.exists('evolve.txt') else 0):
+            # download evolve.txt if larger than local
+            os.system('gsutil cp %s .' % url)
+
+    with open('evolve.txt', 'a') as f:  # append result
+        f.write(c + b + '\n')
+    x = np.unique(np.loadtxt('evolve.txt', ndmin=2),
+                  axis=0)  # load unique rows
+    x = x[np.argsort(-fitness(x))]  # sort
+    np.savetxt('evolve.txt', x, '%10.3g')  # save sort by fitness
+
+    # Save yaml
+    for i, k in enumerate(hyp.keys()):
+        hyp[k] = float(x[0, i + 7])
+    with open(yaml_file, 'w') as f:
+        results = tuple(x[0, :7])
+        # results (P, R, mAP@0.5, mAP@0.5:0.95, val_losses x 3)
+        c = '%10.4g' * len(results) % results
+        f.write('# Hyperparameter Evolution Results\n# Generations: %g\n# Metrics: ' % len(
+            x) + c + '\n\n')
+        yaml.dump(hyp, f, sort_keys=False)
+
+    if bucket:
+        os.system('gsutil cp evolve.txt %s gs://%s' %
+                  (yaml_file, bucket))  # upload
+
+
+def apply_classifier(x, model, img, im0):
+    # applies a second stage classifier to yolo outputs
+    im0 = [im0] if isinstance(im0, np.ndarray) else im0
+    for i, d in enumerate(x):  # per image
+        if d is not None and len(d):
+            d = d.clone()
+
+            # Reshape and pad cutouts
+            b = xyxy2xywh(d[:, :4])  # boxes
+            b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1)  # rectangle to square
+            b[:, 2:] = b[:, 2:] * 1.3 + 30  # pad
+            d[:, :4] = xywh2xyxy(b).long()
+
+            # Rescale boxes from img_size to im0 size
+            scale_coords(img.shape[2:], d[:, :4], im0[i].shape)
+
+            # Classes
+            pred_cls1 = d[:, 5].long()
+            ims = []
+            for j, a in enumerate(d):  # per item
+                cutout = im0[i][int(a[1]):int(a[3]), int(a[0]):int(a[2])]
+                im = cv2.resize(cutout, (224, 224))  # BGR
+                # cv2.imwrite('test%i.jpg' % j, cutout)
+
+                # BGR to RGB, to 3x416x416
+                im = im[:, :, ::-1].transpose(2, 0, 1)
+                im = np.ascontiguousarray(
+                    im, dtype=np.float32)  # uint8 to float32
+                im /= 255.0  # 0 - 255 to 0.0 - 1.0
+                ims.append(im)
+
+            pred_cls2 = model(torch.Tensor(ims).to(d.device)
+                              ).argmax(1)  # classifier prediction
+            # retain matching class detections
+            x[i] = x[i][pred_cls1 == pred_cls2]
+
+    return x
+
+
+def increment_path(path, exist_ok=True, sep=''):
+    # Increment path, i.e. runs/exp --> runs/exp{sep}0, runs/exp{sep}1 etc.
+    path = Path(path)  # os-agnostic
+    if (path.exists() and exist_ok) or (not path.exists()):
+        return str(path)
+    else:
+        dirs = glob.glob(f"{path}{sep}*")  # similar paths
+        matches = [re.search(rf"%s{sep}(\d+)" % path.stem, d) for d in dirs]
+        i = [int(m.groups()[0]) for m in matches if m]  # indices
+        n = max(i) + 1 if i else 2  # increment number
+        return f"{path}{sep}{n}"  # update path
diff --git a/model_image/yolor/utils/google_utils.py b/model_image/yolor/utils/google_utils.py
new file mode 100644
index 0000000..0ff8bbd
--- /dev/null
+++ b/model_image/yolor/utils/google_utils.py
@@ -0,0 +1,120 @@
+# Google utils: https://cloud.google.com/storage/docs/reference/libraries
+
+import os
+import platform
+import subprocess
+import time
+from pathlib import Path
+
+import torch
+
+
+def gsutil_getsize(url=''):
+    # gs://bucket/file size https://cloud.google.com/storage/docs/gsutil/commands/du
+    s = subprocess.check_output('gsutil du %s' % url, shell=True).decode('utf-8')
+    return eval(s.split(' ')[0]) if len(s) else 0  # bytes
+
+
+def attempt_download(weights):
+    # Attempt to download pretrained weights if not found locally
+    weights = weights.strip().replace("'", '')
+    file = Path(weights).name
+
+    msg = weights + ' missing, try downloading from https://github.com/WongKinYiu/yolor/releases/'
+    models = ['yolor_p6.pt', 'yolor_w6.pt']  # available models
+
+    if file in models and not os.path.isfile(weights):
+
+        try:  # GitHub
+            url = 'https://github.com/WongKinYiu/yolor/releases/download/v1.0/' + file
+            print('Downloading %s to %s...' % (url, weights))
+            torch.hub.download_url_to_file(url, weights)
+            assert os.path.exists(weights) and os.path.getsize(weights) > 1E6  # check
+        except Exception as e:  # GCP
+            print('ERROR: Download failure.')
+            print('')
+            
+            
+def attempt_load(weights, map_location=None):
+    # Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a
+    model = Ensemble()
+    for w in weights if isinstance(weights, list) else [weights]:
+        attempt_download(w)
+        model.append(torch.load(w, map_location=map_location)['model'].float().fuse().eval())  # load FP32 model
+
+    if len(model) == 1:
+        return model[-1]  # return model
+    else:
+        print('Ensemble created with %s\n' % weights)
+        for k in ['names', 'stride']:
+            setattr(model, k, getattr(model[-1], k))
+        return model  # return ensemble
+
+
+def gdrive_download(id='1n_oKgR81BJtqk75b00eAjdv03qVCQn2f', name='coco128.zip'):
+    # Downloads a file from Google Drive. from utils.google_utils import *; gdrive_download()
+    t = time.time()
+
+    print('Downloading https://drive.google.com/uc?export=download&id=%s as %s... ' % (id, name), end='')
+    os.remove(name) if os.path.exists(name) else None  # remove existing
+    os.remove('cookie') if os.path.exists('cookie') else None
+
+    # Attempt file download
+    out = "NUL" if platform.system() == "Windows" else "/dev/null"
+    os.system('curl -c ./cookie -s -L "drive.google.com/uc?export=download&id=%s" > %s ' % (id, out))
+    if os.path.exists('cookie'):  # large file
+        s = 'curl -Lb ./cookie "drive.google.com/uc?export=download&confirm=%s&id=%s" -o %s' % (get_token(), id, name)
+    else:  # small file
+        s = 'curl -s -L -o %s "drive.google.com/uc?export=download&id=%s"' % (name, id)
+    r = os.system(s)  # execute, capture return
+    os.remove('cookie') if os.path.exists('cookie') else None
+
+    # Error check
+    if r != 0:
+        os.remove(name) if os.path.exists(name) else None  # remove partial
+        print('Download error ')  # raise Exception('Download error')
+        return r
+
+    # Unzip if archive
+    if name.endswith('.zip'):
+        print('unzipping... ', end='')
+        os.system('unzip -q %s' % name)  # unzip
+        os.remove(name)  # remove zip to free space
+
+    print('Done (%.1fs)' % (time.time() - t))
+    return r
+
+
+def get_token(cookie="./cookie"):
+    with open(cookie) as f:
+        for line in f:
+            if "download" in line:
+                return line.split()[-1]
+    return ""
+
+# def upload_blob(bucket_name, source_file_name, destination_blob_name):
+#     # Uploads a file to a bucket
+#     # https://cloud.google.com/storage/docs/uploading-objects#storage-upload-object-python
+#
+#     storage_client = storage.Client()
+#     bucket = storage_client.get_bucket(bucket_name)
+#     blob = bucket.blob(destination_blob_name)
+#
+#     blob.upload_from_filename(source_file_name)
+#
+#     print('File {} uploaded to {}.'.format(
+#         source_file_name,
+#         destination_blob_name))
+#
+#
+# def download_blob(bucket_name, source_blob_name, destination_file_name):
+#     # Uploads a blob from a bucket
+#     storage_client = storage.Client()
+#     bucket = storage_client.get_bucket(bucket_name)
+#     blob = bucket.blob(source_blob_name)
+#
+#     blob.download_to_filename(destination_file_name)
+#
+#     print('Blob {} downloaded to {}.'.format(
+#         source_blob_name,
+#         destination_file_name))
diff --git a/model_image/yolor/utils/layers.py b/model_image/yolor/utils/layers.py
new file mode 100644
index 0000000..123fb53
--- /dev/null
+++ b/model_image/yolor/utils/layers.py
@@ -0,0 +1,547 @@
+import torch.nn.functional as F
+
+from yolor.utils.general import *
+
+import torch
+from torch import nn
+
+
+class Mish(nn.Module):  # https://github.com/digantamisra98/Mish
+    def forward(self, x):
+        return x * F.softplus(x).tanh()
+
+
+class DWT(nn.Module):
+    def forward(self, x):
+        return torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1)
+
+
+class Reorg(nn.Module):
+    def forward(self, x):
+        return torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1)
+
+
+def make_divisible(v, divisor):
+    # Function ensures all layers have a channel number that is divisible by 8
+    # https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
+    return math.ceil(v / divisor) * divisor
+
+
+class Flatten(nn.Module):
+    # Use after nn.AdaptiveAvgPool2d(1) to remove last 2 dimensions
+    def forward(self, x):
+        return x.view(x.size(0), -1)
+
+
+class Concat(nn.Module):
+    # Concatenate a list of tensors along dimension
+    def __init__(self, dimension=1):
+        super(Concat, self).__init__()
+        self.d = dimension
+
+    def forward(self, x):
+        return torch.cat(x, self.d)
+
+
+class FeatureConcat(nn.Module):
+    def __init__(self, layers):
+        super(FeatureConcat, self).__init__()
+        self.layers = layers  # layer indices
+        self.multiple = len(layers) > 1  # multiple layers flag
+
+    def forward(self, x, outputs):
+        return torch.cat([outputs[i] for i in self.layers], 1) if self.multiple else outputs[self.layers[0]]
+
+
+class FeatureConcat2(nn.Module):
+    def __init__(self, layers):
+        super(FeatureConcat2, self).__init__()
+        self.layers = layers  # layer indices
+        self.multiple = len(layers) > 1  # multiple layers flag
+
+    def forward(self, x, outputs):
+        return torch.cat([outputs[self.layers[0]], outputs[self.layers[1]].detach()], 1)
+
+
+class FeatureConcat3(nn.Module):
+    def __init__(self, layers):
+        super(FeatureConcat3, self).__init__()
+        self.layers = layers  # layer indices
+        self.multiple = len(layers) > 1  # multiple layers flag
+
+    def forward(self, x, outputs):
+        return torch.cat([outputs[self.layers[0]], outputs[self.layers[1]].detach(), outputs[self.layers[2]].detach()], 1)
+
+
+class FeatureConcat_l(nn.Module):
+    def __init__(self, layers):
+        super(FeatureConcat_l, self).__init__()
+        self.layers = layers  # layer indices
+        self.multiple = len(layers) > 1  # multiple layers flag
+
+    def forward(self, x, outputs):
+        return torch.cat([outputs[i][:, :outputs[i].shape[1]//2, :, :] for i in self.layers], 1) if self.multiple else outputs[self.layers[0]][:, :outputs[self.layers[0]].shape[1]//2, :, :]
+
+
+# weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070
+class WeightedFeatureFusion(nn.Module):
+    def __init__(self, layers, weight=False):
+        super(WeightedFeatureFusion, self).__init__()
+        self.layers = layers  # layer indices
+        self.weight = weight  # apply weights boolean
+        self.n = len(layers) + 1  # number of layers
+        if weight:
+            self.w = nn.Parameter(torch.zeros(
+                self.n), requires_grad=True)  # layer weights
+
+    def forward(self, x, outputs):
+        # Weights
+        if self.weight:
+            w = torch.sigmoid(self.w) * (2 / self.n)  # sigmoid weights (0-1)
+            x = x * w[0]
+
+        # Fusion
+        nx = x.shape[1]  # input channels
+        for i in range(self.n - 1):
+            # feature to add
+            a = outputs[self.layers[i]] * \
+                w[i + 1] if self.weight else outputs[self.layers[i]]
+            na = a.shape[1]  # feature channels
+
+            # Adjust channels
+            if nx == na:  # same shape
+                x = x + a
+            elif nx > na:  # slice input
+                # or a = nn.ZeroPad2d((0, 0, 0, 0, 0, dc))(a); x = x + a
+                x[:, :na] = x[:, :na] + a
+            else:  # slice feature
+                x = x + a[:, :nx]
+
+        return x
+
+
+# MixConv: Mixed Depthwise Convolutional Kernels https://arxiv.org/abs/1907.09595
+class MixConv2d(nn.Module):
+    def __init__(self, in_ch, out_ch, k=(3, 5, 7), stride=1, dilation=1, bias=True, method='equal_params'):
+        super(MixConv2d, self).__init__()
+
+        groups = len(k)
+        if method == 'equal_ch':  # equal channels per group
+            # out_ch indices
+            i = torch.linspace(0, groups - 1E-6, out_ch).floor()
+            ch = [(i == g).sum() for g in range(groups)]
+        else:  # 'equal_params': equal parameter count per group
+            b = [out_ch] + [0] * groups
+            a = np.eye(groups + 1, groups, k=-1)
+            a -= np.roll(a, 1, axis=1)
+            a *= np.array(k) ** 2
+            a[0] = 1
+            ch = np.linalg.lstsq(a, b, rcond=None)[0].round().astype(
+                int)  # solve for equal weight indices, ax = b
+
+        self.m = nn.ModuleList([nn.Conv2d(in_channels=in_ch,
+                                          out_channels=ch[g],
+                                          kernel_size=k[g],
+                                          stride=stride,
+                                          padding=k[g] // 2,  # 'same' pad
+                                          dilation=dilation,
+                                          bias=bias) for g in range(groups)])
+
+    def forward(self, x):
+        return torch.cat([m(x) for m in self.m], 1)
+
+
+# Activation functions below -------------------------------------------------------------------------------------------
+class SwishImplementation(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, x):
+        ctx.save_for_backward(x)
+        return x * torch.sigmoid(x)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        x = ctx.saved_tensors[0]
+        sx = torch.sigmoid(x)  # sigmoid(ctx)
+        return grad_output * (sx * (1 + x * (1 - sx)))
+
+
+class MishImplementation(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, x):
+        ctx.save_for_backward(x)
+        return x.mul(torch.tanh(F.softplus(x)))  # x * tanh(ln(1 + exp(x)))
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        x = ctx.saved_tensors[0]
+        sx = torch.sigmoid(x)
+        fx = F.softplus(x).tanh()
+        return grad_output * (fx + x * sx * (1 - fx * fx))
+
+
+class MemoryEfficientSwish(nn.Module):
+    def forward(self, x):
+        return SwishImplementation.apply(x)
+
+
+class MemoryEfficientMish(nn.Module):
+    def forward(self, x):
+        return MishImplementation.apply(x)
+
+
+class Swish(nn.Module):
+    def forward(self, x):
+        return x * torch.sigmoid(x)
+
+
+class HardSwish(nn.Module):  # https://arxiv.org/pdf/1905.02244.pdf
+    def forward(self, x):
+        return x * F.hardtanh(x + 3, 0., 6., True) / 6.
+
+
+class DeformConv2d(nn.Module):
+    def __init__(self, inc, outc, kernel_size=3, padding=1, stride=1, bias=None, modulation=False):
+        """
+        Args:
+            modulation (bool, optional): If True, Modulated Defomable Convolution (Deformable ConvNets v2).
+        """
+        super(DeformConv2d, self).__init__()
+        self.kernel_size = kernel_size
+        self.padding = padding
+        self.stride = stride
+        self.zero_padding = nn.ZeroPad2d(padding)
+        self.conv = nn.Conv2d(
+            inc, outc, kernel_size=kernel_size, stride=kernel_size, bias=bias)
+
+        self.p_conv = nn.Conv2d(
+            inc, 2*kernel_size*kernel_size, kernel_size=3, padding=1, stride=stride)
+        nn.init.constant_(self.p_conv.weight, 0)
+        self.p_conv.register_backward_hook(self._set_lr)
+
+        self.modulation = modulation
+        if modulation:
+            self.m_conv = nn.Conv2d(
+                inc, kernel_size*kernel_size, kernel_size=3, padding=1, stride=stride)
+            nn.init.constant_(self.m_conv.weight, 0)
+            self.m_conv.register_backward_hook(self._set_lr)
+
+    @staticmethod
+    def _set_lr(module, grad_input, grad_output):
+        grad_input = (grad_input[i] * 0.1 for i in range(len(grad_input)))
+        grad_output = (grad_output[i] * 0.1 for i in range(len(grad_output)))
+
+    def forward(self, x):
+        offset = self.p_conv(x)
+        if self.modulation:
+            m = torch.sigmoid(self.m_conv(x))
+
+        dtype = offset.data.type()
+        ks = self.kernel_size
+        N = offset.size(1) // 2
+
+        if self.padding:
+            x = self.zero_padding(x)
+
+        # (b, 2N, h, w)
+        p = self._get_p(offset, dtype)
+
+        # (b, h, w, 2N)
+        p = p.contiguous().permute(0, 2, 3, 1)
+        q_lt = p.detach().floor()
+        q_rb = q_lt + 1
+
+        q_lt = torch.cat([torch.clamp(q_lt[..., :N], 0, x.size(
+            2)-1), torch.clamp(q_lt[..., N:], 0, x.size(3)-1)], dim=-1).long()
+        q_rb = torch.cat([torch.clamp(q_rb[..., :N], 0, x.size(
+            2)-1), torch.clamp(q_rb[..., N:], 0, x.size(3)-1)], dim=-1).long()
+        q_lb = torch.cat([q_lt[..., :N], q_rb[..., N:]], dim=-1)
+        q_rt = torch.cat([q_rb[..., :N], q_lt[..., N:]], dim=-1)
+
+        # clip p
+        p = torch.cat([torch.clamp(p[..., :N], 0, x.size(2)-1),
+                      torch.clamp(p[..., N:], 0, x.size(3)-1)], dim=-1)
+
+        # bilinear kernel (b, h, w, N)
+        g_lt = (1 + (q_lt[..., :N].type_as(p) - p[..., :N])) * \
+            (1 + (q_lt[..., N:].type_as(p) - p[..., N:]))
+        g_rb = (1 - (q_rb[..., :N].type_as(p) - p[..., :N])) * \
+            (1 - (q_rb[..., N:].type_as(p) - p[..., N:]))
+        g_lb = (1 + (q_lb[..., :N].type_as(p) - p[..., :N])) * \
+            (1 - (q_lb[..., N:].type_as(p) - p[..., N:]))
+        g_rt = (1 - (q_rt[..., :N].type_as(p) - p[..., :N])) * \
+            (1 + (q_rt[..., N:].type_as(p) - p[..., N:]))
+
+        # (b, c, h, w, N)
+        x_q_lt = self._get_x_q(x, q_lt, N)
+        x_q_rb = self._get_x_q(x, q_rb, N)
+        x_q_lb = self._get_x_q(x, q_lb, N)
+        x_q_rt = self._get_x_q(x, q_rt, N)
+
+        # (b, c, h, w, N)
+        x_offset = g_lt.unsqueeze(dim=1) * x_q_lt + \
+            g_rb.unsqueeze(dim=1) * x_q_rb + \
+            g_lb.unsqueeze(dim=1) * x_q_lb + \
+            g_rt.unsqueeze(dim=1) * x_q_rt
+
+        # modulation
+        if self.modulation:
+            m = m.contiguous().permute(0, 2, 3, 1)
+            m = m.unsqueeze(dim=1)
+            m = torch.cat([m for _ in range(x_offset.size(1))], dim=1)
+            x_offset *= m
+
+        x_offset = self._reshape_x_offset(x_offset, ks)
+        out = self.conv(x_offset)
+
+        return out
+
+    def _get_p_n(self, N, dtype):
+        p_n_x, p_n_y = torch.meshgrid(
+            torch.arange(-(self.kernel_size-1)//2, (self.kernel_size-1)//2+1),
+            torch.arange(-(self.kernel_size-1)//2, (self.kernel_size-1)//2+1))
+        # (2N, 1)
+        p_n = torch.cat([torch.flatten(p_n_x), torch.flatten(p_n_y)], 0)
+        p_n = p_n.view(1, 2*N, 1, 1).type(dtype)
+
+        return p_n
+
+    def _get_p_0(self, h, w, N, dtype):
+        p_0_x, p_0_y = torch.meshgrid(
+            torch.arange(1, h*self.stride+1, self.stride),
+            torch.arange(1, w*self.stride+1, self.stride))
+        p_0_x = torch.flatten(p_0_x).view(1, 1, h, w).repeat(1, N, 1, 1)
+        p_0_y = torch.flatten(p_0_y).view(1, 1, h, w).repeat(1, N, 1, 1)
+        p_0 = torch.cat([p_0_x, p_0_y], 1).type(dtype)
+
+        return p_0
+
+    def _get_p(self, offset, dtype):
+        N, h, w = offset.size(1)//2, offset.size(2), offset.size(3)
+
+        # (1, 2N, 1, 1)
+        p_n = self._get_p_n(N, dtype)
+        # (1, 2N, h, w)
+        p_0 = self._get_p_0(h, w, N, dtype)
+        p = p_0 + p_n + offset
+        return p
+
+    def _get_x_q(self, x, q, N):
+        b, h, w, _ = q.size()
+        padded_w = x.size(3)
+        c = x.size(1)
+        # (b, c, h*w)
+        x = x.contiguous().view(b, c, -1)
+
+        # (b, h, w, N)
+        index = q[..., :N]*padded_w + q[..., N:]  # offset_x*w + offset_y
+        # (b, c, h*w*N)
+        index = index.contiguous().unsqueeze(dim=1).expand(-1, c, -
+                                                           1, -1, -1).contiguous().view(b, c, -1)
+
+        x_offset = x.gather(
+            dim=-1, index=index).contiguous().view(b, c, h, w, N)
+
+        return x_offset
+
+    @staticmethod
+    def _reshape_x_offset(x_offset, ks):
+        b, c, h, w, N = x_offset.size()
+        x_offset = torch.cat(
+            [x_offset[..., s:s+ks].contiguous().view(b, c, h, w*ks) for s in range(0, N, ks)], dim=-1)
+        x_offset = x_offset.contiguous().view(b, c, h*ks, w*ks)
+
+        return x_offset
+
+
+class GAP(nn.Module):
+    def __init__(self):
+        super(GAP, self).__init__()
+        self.avg_pool = nn.AdaptiveAvgPool2d(1)
+
+    def forward(self, x):
+        # b, c, _, _ = x.size()
+        return self.avg_pool(x)  # .view(b, c)
+
+
+class Silence(nn.Module):
+    def __init__(self):
+        super(Silence, self).__init__()
+
+    def forward(self, x):
+        return x
+
+
+# weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070
+class ScaleChannel(nn.Module):
+    def __init__(self, layers):
+        super(ScaleChannel, self).__init__()
+        self.layers = layers  # layer indices
+
+    def forward(self, x, outputs):
+        a = outputs[self.layers[0]]
+        return x.expand_as(a) * a
+
+
+# weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070
+class ShiftChannel(nn.Module):
+    def __init__(self, layers):
+        super(ShiftChannel, self).__init__()
+        self.layers = layers  # layer indices
+
+    def forward(self, x, outputs):
+        a = outputs[self.layers[0]]
+        return a.expand_as(x) + x
+
+
+# weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070
+class ShiftChannel2D(nn.Module):
+    def __init__(self, layers):
+        super(ShiftChannel2D, self).__init__()
+        self.layers = layers  # layer indices
+
+    def forward(self, x, outputs):
+        a = outputs[self.layers[0]].view(1, -1, 1, 1)
+        return a.expand_as(x) + x
+
+
+# weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070
+class ControlChannel(nn.Module):
+    def __init__(self, layers):
+        super(ControlChannel, self).__init__()
+        self.layers = layers  # layer indices
+
+    def forward(self, x, outputs):
+        a = outputs[self.layers[0]]
+        return a.expand_as(x) * x
+
+
+# weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070
+class ControlChannel2D(nn.Module):
+    def __init__(self, layers):
+        super(ControlChannel2D, self).__init__()
+        self.layers = layers  # layer indices
+
+    def forward(self, x, outputs):
+        a = outputs[self.layers[0]].view(1, -1, 1, 1)
+        return a.expand_as(x) * x
+
+
+# weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070
+class AlternateChannel(nn.Module):
+    def __init__(self, layers):
+        super(AlternateChannel, self).__init__()
+        self.layers = layers  # layer indices
+
+    def forward(self, x, outputs):
+        a = outputs[self.layers[0]]
+        return torch.cat([a.expand_as(x), x], dim=1)
+
+
+# weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070
+class AlternateChannel2D(nn.Module):
+    def __init__(self, layers):
+        super(AlternateChannel2D, self).__init__()
+        self.layers = layers  # layer indices
+
+    def forward(self, x, outputs):
+        a = outputs[self.layers[0]].view(1, -1, 1, 1)
+        return torch.cat([a.expand_as(x), x], dim=1)
+
+
+# weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070
+class SelectChannel(nn.Module):
+    def __init__(self, layers):
+        super(SelectChannel, self).__init__()
+        self.layers = layers  # layer indices
+
+    def forward(self, x, outputs):
+        a = outputs[self.layers[0]]
+        return a.sigmoid().expand_as(x) * x
+
+
+# weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070
+class SelectChannel2D(nn.Module):
+    def __init__(self, layers):
+        super(SelectChannel2D, self).__init__()
+        self.layers = layers  # layer indices
+
+    def forward(self, x, outputs):
+        a = outputs[self.layers[0]].view(1, -1, 1, 1)
+        return a.sigmoid().expand_as(x) * x
+
+
+# weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070
+class ScaleSpatial(nn.Module):
+    def __init__(self, layers):
+        super(ScaleSpatial, self).__init__()
+        self.layers = layers  # layer indices
+
+    def forward(self, x, outputs):
+        a = outputs[self.layers[0]]
+        return x * a
+
+
+class ImplicitA(nn.Module):
+    def __init__(self, channel):
+        super(ImplicitA, self).__init__()
+        self.channel = channel
+        self.implicit = nn.Parameter(torch.zeros(1, channel, 1, 1))
+        nn.init.normal_(self.implicit, std=.02)
+
+    def forward(self):
+        return self.implicit
+
+
+class ImplicitC(nn.Module):
+    def __init__(self, channel):
+        super(ImplicitC, self).__init__()
+        self.channel = channel
+        self.implicit = nn.Parameter(torch.zeros(1, channel, 1, 1))
+        nn.init.normal_(self.implicit, std=.02)
+
+    def forward(self):
+        return self.implicit
+
+
+class ImplicitM(nn.Module):
+    def __init__(self, channel):
+        super(ImplicitM, self).__init__()
+        self.channel = channel
+        self.implicit = nn.Parameter(torch.ones(1, channel, 1, 1))
+        nn.init.normal_(self.implicit, mean=1., std=.02)
+
+    def forward(self):
+        return self.implicit
+
+
+class Implicit2DA(nn.Module):
+    def __init__(self, atom, channel):
+        super(Implicit2DA, self).__init__()
+        self.channel = channel
+        self.implicit = nn.Parameter(torch.zeros(1, atom, channel, 1))
+        nn.init.normal_(self.implicit, std=.02)
+
+    def forward(self):
+        return self.implicit
+
+
+class Implicit2DC(nn.Module):
+    def __init__(self, atom, channel):
+        super(Implicit2DC, self).__init__()
+        self.channel = channel
+        self.implicit = nn.Parameter(torch.zeros(1, atom, channel, 1))
+        nn.init.normal_(self.implicit, std=.02)
+
+    def forward(self):
+        return self.implicit
+
+
+class Implicit2DM(nn.Module):
+    def __init__(self, atom, channel):
+        super(Implicit2DM, self).__init__()
+        self.channel = channel
+        self.implicit = nn.Parameter(torch.ones(1, atom, channel, 1))
+        nn.init.normal_(self.implicit, mean=1., std=.02)
+
+    def forward(self):
+        return self.implicit
diff --git a/model_image/yolor/utils/loss.py b/model_image/yolor/utils/loss.py
new file mode 100644
index 0000000..284a22e
--- /dev/null
+++ b/model_image/yolor/utils/loss.py
@@ -0,0 +1,186 @@
+# Loss functions
+
+import torch
+import torch.nn as nn
+
+from yolor.utils.general import bbox_iou
+from yolor.utils.torch_utils import is_parallel
+
+
+def smooth_BCE(eps=0.1):  # https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441
+    # return positive, negative label smoothing BCE targets
+    return 1.0 - 0.5 * eps, 0.5 * eps
+
+
+class BCEBlurWithLogitsLoss(nn.Module):
+    # BCEwithLogitLoss() with reduced missing label effects.
+    def __init__(self, alpha=0.05):
+        super(BCEBlurWithLogitsLoss, self).__init__()
+        self.loss_fcn = nn.BCEWithLogitsLoss(
+            reduction='none')  # must be nn.BCEWithLogitsLoss()
+        self.alpha = alpha
+
+    def forward(self, pred, true):
+        loss = self.loss_fcn(pred, true)
+        pred = torch.sigmoid(pred)  # prob from logits
+        dx = pred - true  # reduce only missing label effects
+        # dx = (pred - true).abs()  # reduce missing label and false label effects
+        alpha_factor = 1 - torch.exp((dx - 1) / (self.alpha + 1e-4))
+        loss *= alpha_factor
+        return loss.mean()
+
+
+class FocalLoss(nn.Module):
+    # Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)
+    def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
+        super(FocalLoss, self).__init__()
+        self.loss_fcn = loss_fcn  # must be nn.BCEWithLogitsLoss()
+        self.gamma = gamma
+        self.alpha = alpha
+        self.reduction = loss_fcn.reduction
+        self.loss_fcn.reduction = 'none'  # required to apply FL to each element
+
+    def forward(self, pred, true):
+        loss = self.loss_fcn(pred, true)
+        # p_t = torch.exp(-loss)
+        # loss *= self.alpha * (1.000001 - p_t) ** self.gamma  # non-zero power for gradient stability
+
+        # TF implementation https://github.com/tensorflow/addons/blob/v0.7.1/tensorflow_addons/losses/focal_loss.py
+        pred_prob = torch.sigmoid(pred)  # prob from logits
+        p_t = true * pred_prob + (1 - true) * (1 - pred_prob)
+        alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha)
+        modulating_factor = (1.0 - p_t) ** self.gamma
+        loss *= alpha_factor * modulating_factor
+
+        if self.reduction == 'mean':
+            return loss.mean()
+        elif self.reduction == 'sum':
+            return loss.sum()
+        else:  # 'none'
+            return loss
+
+
+def compute_loss(p, targets, model):  # predictions, targets, model
+    device = targets.device
+    # print(device)
+    lcls, lbox, lobj = torch.zeros(1, device=device), torch.zeros(
+        1, device=device), torch.zeros(1, device=device)
+    tcls, tbox, indices, anchors = build_targets(p, targets, model)  # targets
+    h = model.hyp  # hyperparameters
+
+    # Define criteria
+    BCEcls = nn.BCEWithLogitsLoss(
+        pos_weight=torch.Tensor([h['cls_pw']])).to(device)
+    BCEobj = nn.BCEWithLogitsLoss(
+        pos_weight=torch.Tensor([h['obj_pw']])).to(device)
+
+    # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
+    cp, cn = smooth_BCE(eps=0.0)
+
+    # Focal loss
+    g = h['fl_gamma']  # focal loss gamma
+    if g > 0:
+        BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)
+
+    # Losses
+    nt = 0  # number of targets
+    no = len(p)  # number of outputs
+    balance = [4.0, 1.0, 0.4] if no == 3 else [
+        4.0, 1.0, 0.4, 0.1]  # P3-5 or P3-6
+    balance = [4.0, 1.0, 0.5, 0.4, 0.1] if no == 5 else balance
+    for i, pi in enumerate(p):  # layer index, layer predictions
+        b, a, gj, gi = indices[i]  # image, anchor, gridy, gridx
+        tobj = torch.zeros_like(pi[..., 0], device=device)  # target obj
+
+        n = b.shape[0]  # number of targets
+        if n:
+            nt += n  # cumulative targets
+            ps = pi[b, a, gj, gi]  # prediction subset corresponding to targets
+
+            # Regression
+            pxy = ps[:, :2].sigmoid() * 2. - 0.5
+            pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i]
+            pbox = torch.cat((pxy, pwh), 1).to(device)  # predicted box
+            # iou(prediction, target)
+            iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True)
+            lbox += (1.0 - iou).mean()  # iou loss
+
+            # Objectness
+            tobj[b, a, gj, gi] = (1.0 - model.gr) + model.gr * \
+                iou.detach().clamp(0).type(tobj.dtype)  # iou ratio
+
+            # Classification
+            if model.nc > 1:  # cls loss (only if multiple classes)
+                t = torch.full_like(ps[:, 5:], cn, device=device)  # targets
+                t[range(n), tcls[i]] = cp
+                lcls += BCEcls(ps[:, 5:], t)  # BCE
+
+            # Append targets to text file
+            # with open('targets.txt', 'a') as file:
+            #     [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)]
+
+        lobj += BCEobj(pi[..., 4], tobj) * balance[i]  # obj loss
+
+    s = 3 / no  # output count scaling
+    lbox *= h['box'] * s
+    lobj *= h['obj'] * s * (1.4 if no >= 4 else 1.)
+    lcls *= h['cls'] * s
+    bs = tobj.shape[0]  # batch size
+
+    loss = lbox + lobj + lcls
+    return loss * bs, torch.cat((lbox, lobj, lcls, loss)).detach()
+
+
+def build_targets(p, targets, model):
+    nt = targets.shape[0]  # number of anchors, targets
+    tcls, tbox, indices, anch = [], [], [], []
+    gain = torch.ones(6, device=targets.device)  # normalized to gridspace gain
+    off = torch.tensor([[1, 0], [0, 1], [-1, 0], [0, -1]],
+                       device=targets.device).float()  # overlap offsets
+
+    g = 0.5  # offset
+    multi_gpu = is_parallel(model)
+    for i, jj in enumerate(model.module.yolo_layers if multi_gpu else model.yolo_layers):
+        # get number of grid points and anchor vec for this yolo layer
+        anchors = model.module.module_list[jj].anchor_vec if multi_gpu else model.module_list[jj].anchor_vec
+        gain[2:] = torch.tensor(p[i].shape)[[3, 2, 3, 2]]  # xyxy gain
+
+        # Match targets to anchors
+        a, t, offsets = [], targets * gain, 0
+        if nt:
+            na = anchors.shape[0]  # number of anchors
+            # anchor tensor, same as .repeat_interleave(nt)
+            at = torch.arange(na).view(na, 1).repeat(1, nt)
+            r = t[None, :, 4:6] / anchors[:, None]  # wh ratio
+            j = torch.max(
+                r, 1. / r).max(2)[0] < model.hyp['anchor_t']  # compare
+            # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t']  # iou(3,n) = wh_iou(anchors(3,2), gwh(n,2))
+            a, t = at[j], t.repeat(na, 1, 1)[j]  # filter
+
+            # overlaps
+            gxy = t[:, 2:4]  # grid xy
+            z = torch.zeros_like(gxy)
+            j, k = ((gxy % 1. < g) & (gxy > 1.)).T
+            l, m = ((gxy % 1. > (1 - g)) & (gxy < (gain[[2, 3]] - 1.))).T
+            a, t = torch.cat((a, a[j], a[k], a[l], a[m]), 0), torch.cat(
+                (t, t[j], t[k], t[l], t[m]), 0)
+            offsets = torch.cat(
+                (z, z[j] + off[0], z[k] + off[1], z[l] + off[2], z[m] + off[3]), 0) * g
+
+        # Define
+        b, c = t[:, :2].long().T  # image, class
+        gxy = t[:, 2:4]  # grid xy
+        gwh = t[:, 4:6]  # grid wh
+        gij = (gxy - offsets).long()
+        gi, gj = gij.T  # grid xy indices
+
+        # Append
+        # indices.append((b, a, gj, gi))  # image, anchor, grid indices
+        # image, anchor, grid indices
+        indices.append(
+            (b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1)))
+        tbox.append(torch.cat((gxy - gij, gwh), 1))  # box
+        anch.append(anchors[a])  # anchors
+        tcls.append(c)  # class
+
+    return tcls, tbox, indices, anch
diff --git a/model_image/yolor/utils/metrics.py b/model_image/yolor/utils/metrics.py
new file mode 100644
index 0000000..fa25849
--- /dev/null
+++ b/model_image/yolor/utils/metrics.py
@@ -0,0 +1,146 @@
+# Model validation metrics
+
+import numpy as np
+
+
+def fitness(x):
+    # Model fitness as a weighted combination of metrics
+    w = [0.0, 0.0, 0.1, 0.9]  # weights for [P, R, mAP@0.5, mAP@0.5:0.95]
+    return (x[:, :4] * w).sum(1)
+
+
+def fitness_p(x):
+    # Model fitness as a weighted combination of metrics
+    w = [1.0, 0.0, 0.0, 0.0]  # weights for [P, R, mAP@0.5, mAP@0.5:0.95]
+    return (x[:, :4] * w).sum(1)
+
+
+def fitness_r(x):
+    # Model fitness as a weighted combination of metrics
+    w = [0.0, 1.0, 0.0, 0.0]  # weights for [P, R, mAP@0.5, mAP@0.5:0.95]
+    return (x[:, :4] * w).sum(1)
+
+
+def fitness_ap50(x):
+    # Model fitness as a weighted combination of metrics
+    w = [0.0, 0.0, 1.0, 0.0]  # weights for [P, R, mAP@0.5, mAP@0.5:0.95]
+    return (x[:, :4] * w).sum(1)
+
+
+def fitness_ap(x):
+    # Model fitness as a weighted combination of metrics
+    w = [0.0, 0.0, 0.0, 1.0]  # weights for [P, R, mAP@0.5, mAP@0.5:0.95]
+    return (x[:, :4] * w).sum(1)
+
+
+def fitness_f(x):
+    # Model fitness as a weighted combination of metrics
+    # w = [0.0, 0.0, 0.0, 1.0]  # weights for [P, R, mAP@0.5, mAP@0.5:0.95]
+    return ((x[:, 0]*x[:, 1])/(x[:, 0]+x[:, 1]))
+
+
+def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, fname='precision-recall_curve.png'):
+    """ Compute the average precision, given the recall and precision curves.
+    Source: https://github.com/rafaelpadilla/Object-Detection-Metrics.
+    # Arguments
+        tp:  True positives (nparray, nx1 or nx10).
+        conf:  Objectness value from 0-1 (nparray).
+        pred_cls:  Predicted object classes (nparray).
+        target_cls:  True object classes (nparray).
+        plot:  Plot precision-recall curve at mAP@0.5
+        fname:  Plot filename
+    # Returns
+        The average precision as computed in py-faster-rcnn.
+    """
+
+    # Sort by objectness
+    i = np.argsort(-conf)
+    tp, conf, pred_cls = tp[i], conf[i], pred_cls[i]
+
+    # Find unique classes
+    unique_classes = np.unique(target_cls)
+
+    # Create Precision-Recall curve and compute AP for each class
+    px, py = np.linspace(0, 1, 1000), []  # for plotting
+    pr_score = 0.1  # score to evaluate P and R https://github.com/ultralytics/yolov3/issues/898
+    # number class, number iou thresholds (i.e. 10 for mAP0.5...0.95)
+    s = [unique_classes.shape[0], tp.shape[1]]
+    ap, p, r = np.zeros(s), np.zeros(s), np.zeros(s)
+    for ci, c in enumerate(unique_classes):
+        i = pred_cls == c
+        n_l = (target_cls == c).sum()  # number of labels
+        n_p = i.sum()  # number of predictions
+
+        if n_p == 0 or n_l == 0:
+            continue
+        else:
+            # Accumulate FPs and TPs
+            fpc = (1 - tp[i]).cumsum(0)
+            tpc = tp[i].cumsum(0)
+
+            # Recall
+            recall = tpc / (n_l + 1e-16)  # recall curve
+            # r at pr_score, negative x, xp because xp decreases
+            r[ci] = np.interp(-pr_score, -conf[i], recall[:, 0])
+
+            # Precision
+            precision = tpc / (tpc + fpc)  # precision curve
+            p[ci] = np.interp(-pr_score, -conf[i],
+                              precision[:, 0])  # p at pr_score
+
+            # AP from recall-precision curve
+            for j in range(tp.shape[1]):
+                ap[ci, j], mpre, mrec = compute_ap(
+                    recall[:, j], precision[:, j])
+                if j == 0:
+                    # precision at mAP@0.5
+                    py.append(np.interp(px, mrec, mpre))
+
+    # Compute F1 score (harmonic mean of precision and recall)
+    f1 = 2 * p * r / (p + r + 1e-16)
+
+    if plot:
+        py = np.stack(py, axis=1)
+        fig, ax = plt.subplots(1, 1, figsize=(5, 5))
+        ax.plot(px, py, linewidth=0.5, color='grey')  # plot(recall, precision)
+        ax.plot(px, py.mean(1), linewidth=2, color='blue',
+                label='all classes %.3f mAP@0.5' % ap[:, 0].mean())
+        ax.set_xlabel('Recall')
+        ax.set_ylabel('Precision')
+        ax.set_xlim(0, 1)
+        ax.set_ylim(0, 1)
+        plt.legend()
+        fig.tight_layout()
+        fig.savefig(fname, dpi=200)
+
+    return p, r, ap, f1, unique_classes.astype('int32')
+
+
+def compute_ap(recall, precision):
+    """ Compute the average precision, given the recall and precision curves.
+    Source: https://github.com/rbgirshick/py-faster-rcnn.
+    # Arguments
+        recall:    The recall curve (list).
+        precision: The precision curve (list).
+    # Returns
+        The average precision as computed in py-faster-rcnn.
+    """
+
+    # Append sentinel values to beginning and end
+    mrec = np.concatenate(([0.0], recall, [1.0]))
+    mpre = np.concatenate(([1.0], precision, [0.0]))
+
+    # Compute the precision envelope
+    mpre = np.flip(np.maximum.accumulate(np.flip(mpre)))
+
+    # Integrate area under curve
+    method = 'interp'  # methods: 'continuous', 'interp'
+    if method == 'interp':
+        x = np.linspace(0, 1, 101)  # 101-point interp (COCO)
+        ap = np.trapz(np.interp(x, mrec, mpre), x)  # integrate
+    else:  # 'continuous'
+        # points where x axis (recall) changes
+        i = np.where(mrec[1:] != mrec[:-1])[0]
+        ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])  # area under curve
+
+    return ap, mpre, mrec
diff --git a/model_image/yolor/utils/parse_config.py b/model_image/yolor/utils/parse_config.py
new file mode 100644
index 0000000..4a03a8c
--- /dev/null
+++ b/model_image/yolor/utils/parse_config.py
@@ -0,0 +1,74 @@
+import os
+
+import numpy as np
+
+
+def parse_model_cfg(path):
+    # Parse the yolo *.cfg file and return module definitions path may be 'cfg/yolov3.cfg', 'yolov3.cfg', or 'yolov3'
+
+    with open(path, 'r') as f:
+        lines = f.read().split('\n')
+    lines = [x for x in lines if x and not x.startswith('#')]
+    lines = [x.rstrip().lstrip()
+             for x in lines]  # get rid of fringe whitespaces
+    mdefs = []  # module definitions
+    for line in lines:
+        if line.startswith('['):  # This marks the start of a new block
+            mdefs.append({})
+            mdefs[-1]['type'] = line[1:-1].rstrip()
+            if mdefs[-1]['type'] == 'convolutional':
+                # pre-populate with zeros (may be overwritten later)
+                mdefs[-1]['batch_normalize'] = 0
+
+        else:
+            key, val = line.split("=")
+            key = key.rstrip()
+
+            if key == 'anchors':  # return nparray
+                # np anchors
+                mdefs[-1][key] = np.array([float(x)
+                                          for x in val.split(',')]).reshape((-1, 2))
+            elif (key in ['from', 'layers', 'mask']) or (key == 'size' and ',' in val):  # return array
+                mdefs[-1][key] = [int(x) for x in val.split(',')]
+            else:
+                val = val.strip()
+                if val.isnumeric():  # return int or float
+                    mdefs[-1][key] = int(val) if (int(val) -
+                                                  float(val)) == 0 else float(val)
+                else:
+                    mdefs[-1][key] = val  # return string
+
+    # Check all fields are supported
+    supported = ['type', 'batch_normalize', 'filters', 'size', 'stride', 'pad', 'activation', 'layers', 'groups',
+                 'from', 'mask', 'anchors', 'classes', 'num', 'jitter', 'ignore_thresh', 'truth_thresh', 'random',
+                 'stride_x', 'stride_y', 'weights_type', 'weights_normalization', 'scale_x_y', 'beta_nms', 'nms_kind',
+                 'iou_loss', 'iou_normalizer', 'cls_normalizer', 'iou_thresh', 'atoms', 'na', 'nc']
+
+    f = []  # fields
+    for x in mdefs[1:]:
+        [f.append(k) for k in x if k not in f]
+    u = [x for x in f if x not in supported]  # unsupported fields
+    assert not any(
+        u), "Unsupported fields %s in %s. See https://github.com/ultralytics/yolov3/issues/631" % (u, path)
+
+    return mdefs
+
+
+def parse_data_cfg(path):
+    # Parses the data configuration file
+    # add data/ prefix if omitted
+    if not os.path.exists(path) and os.path.exists('data' + os.sep + path):
+        path = 'data' + os.sep + path
+
+    with open(path, 'r') as f:
+        lines = f.readlines()
+
+    options = dict()
+    for line in lines:
+        line = line.strip()
+        if line == '' or line.startswith('#'):
+            continue
+        key, val = line.split('=')
+        options[key.strip()] = val.strip()
+
+    return options
diff --git a/model_image/yolor/utils/plots.py b/model_image/yolor/utils/plots.py
new file mode 100644
index 0000000..586824b
--- /dev/null
+++ b/model_image/yolor/utils/plots.py
@@ -0,0 +1,404 @@
+# Plotting utils
+
+import glob
+import math
+import os
+import random
+from copy import copy
+from pathlib import Path
+
+import cv2
+import numpy as np
+import torch
+import yaml
+from PIL import Image
+from scipy.signal import butter, filtfilt
+
+from utils.general import xywh2xyxy, xyxy2xywh
+from utils.metrics import fitness
+
+# Settings
+
+
+def color_list():
+    # Return first 10 plt colors as (r,g,b) https://stackoverflow.com/questions/51350872/python-from-color-name-to-rgb
+    def hex2rgb(h):
+        return tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4))
+
+    return [hex2rgb(h) for h in plt.rcParams['axes.prop_cycle'].by_key()['color']]
+
+
+def hist2d(x, y, n=100):
+    # 2d histogram used in labels.png and evolve.png
+    xedges, yedges = np.linspace(
+        x.min(), x.max(), n), np.linspace(y.min(), y.max(), n)
+    hist, xedges, yedges = np.histogram2d(x, y, (xedges, yedges))
+    xidx = np.clip(np.digitize(x, xedges) - 1, 0, hist.shape[0] - 1)
+    yidx = np.clip(np.digitize(y, yedges) - 1, 0, hist.shape[1] - 1)
+    return np.log(hist[xidx, yidx])
+
+
+def butter_lowpass_filtfilt(data, cutoff=1500, fs=50000, order=5):
+    # https://stackoverflow.com/questions/28536191/how-to-filter-smooth-with-scipy-numpy
+    def butter_lowpass(cutoff, fs, order):
+        nyq = 0.5 * fs
+        normal_cutoff = cutoff / nyq
+        return butter(order, normal_cutoff, btype='low', analog=False)
+
+    b, a = butter_lowpass(cutoff, fs, order=order)
+    return filtfilt(b, a, data)  # forward-backward filter
+
+
+def plot_one_box(x, img, color=None, label=None, line_thickness=None):
+    # Plots one bounding box on image img
+    tl = line_thickness or round(
+        0.002 * (img.shape[0] + img.shape[1]) / 2) + 1  # line/font thickness
+    color = color or [random.randint(0, 255) for _ in range(3)]
+    c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
+    cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA)
+    if label:
+        tf = max(tl - 1, 1)  # font thickness
+        t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
+        c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3
+        cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA)  # filled
+        cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3,
+                    [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)
+
+
+def plot_wh_methods():  # from utils.general import *; plot_wh_methods()
+    # Compares the two methods for width-height anchor multiplication
+    # https://github.com/ultralytics/yolov3/issues/168
+    x = np.arange(-4.0, 4.0, .1)
+    ya = np.exp(x)
+    yb = torch.sigmoid(torch.from_numpy(x)).numpy() * 2
+
+    fig = plt.figure(figsize=(6, 3), dpi=150)
+    plt.plot(x, ya, '.-', label='YOLO')
+    plt.plot(x, yb ** 2, '.-', label='YOLO ^2')
+    plt.plot(x, yb ** 1.6, '.-', label='YOLO ^1.6')
+    plt.xlim(left=-4, right=4)
+    plt.ylim(bottom=0, top=6)
+    plt.xlabel('input')
+    plt.ylabel('output')
+    plt.grid()
+    plt.legend()
+    fig.tight_layout()
+    fig.savefig('comparison.png', dpi=200)
+
+
+def output_to_target(output, width, height):
+    # Convert model output to target format [batch_id, class_id, x, y, w, h, conf]
+    if isinstance(output, torch.Tensor):
+        output = output.cpu().numpy()
+
+    targets = []
+    for i, o in enumerate(output):
+        if o is not None:
+            for pred in o:
+                box = pred[:4]
+                w = (box[2] - box[0]) / width
+                h = (box[3] - box[1]) / height
+                x = box[0] / width + w / 2
+                y = box[1] / height + h / 2
+                conf = pred[4]
+                cls = int(pred[5])
+
+                targets.append([i, cls, x, y, w, h, conf])
+
+    return np.array(targets)
+
+
+def plot_images(images, targets, paths=None, fname='images.jpg', names=None, max_size=640, max_subplots=16):
+    # Plot image grid with labels
+
+    if isinstance(images, torch.Tensor):
+        images = images.cpu().float().numpy()
+    if isinstance(targets, torch.Tensor):
+        targets = targets.cpu().numpy()
+
+    # un-normalise
+    if np.max(images[0]) <= 1:
+        images *= 255
+
+    tl = 3  # line thickness
+    tf = max(tl - 1, 1)  # font thickness
+    bs, _, h, w = images.shape  # batch size, _, height, width
+    bs = min(bs, max_subplots)  # limit plot images
+    ns = np.ceil(bs ** 0.5)  # number of subplots (square)
+
+    # Check if we should resize
+    scale_factor = max_size / max(h, w)
+    if scale_factor < 1:
+        h = math.ceil(scale_factor * h)
+        w = math.ceil(scale_factor * w)
+
+    colors = color_list()  # list of colors
+    mosaic = np.full((int(ns * h), int(ns * w), 3),
+                     255, dtype=np.uint8)  # init
+    for i, img in enumerate(images):
+        if i == max_subplots:  # if last batch has fewer images than we expect
+            break
+
+        block_x = int(w * (i // ns))
+        block_y = int(h * (i % ns))
+
+        img = img.transpose(1, 2, 0)
+        if scale_factor < 1:
+            img = cv2.resize(img, (w, h))
+
+        mosaic[block_y:block_y + h, block_x:block_x + w, :] = img
+        if len(targets) > 0:
+            image_targets = targets[targets[:, 0] == i]
+            boxes = xywh2xyxy(image_targets[:, 2:6]).T
+            classes = image_targets[:, 1].astype('int')
+            labels = image_targets.shape[1] == 6  # labels if no conf column
+            # check for confidence presence (label vs pred)
+            conf = None if labels else image_targets[:, 6]
+
+            boxes[[0, 2]] *= w
+            boxes[[0, 2]] += block_x
+            boxes[[1, 3]] *= h
+            boxes[[1, 3]] += block_y
+            for j, box in enumerate(boxes.T):
+                cls = int(classes[j])
+                color = colors[cls % len(colors)]
+                cls = names[cls] if names else cls
+                if labels or conf[j] > 0.25:  # 0.25 conf thresh
+                    label = '%s' % cls if labels else '%s %.1f' % (
+                        cls, conf[j])
+                    plot_one_box(box, mosaic, label=label,
+                                 color=color, line_thickness=tl)
+
+        # Draw image filename labels
+        if paths:
+            label = Path(paths[i]).name[:40]  # trim to 40 char
+            t_size = cv2.getTextSize(
+                label, 0, fontScale=tl / 3, thickness=tf)[0]
+            cv2.putText(mosaic, label, (block_x + 5, block_y + t_size[1] + 5), 0, tl / 3, [220, 220, 220], thickness=tf,
+                        lineType=cv2.LINE_AA)
+
+        # Image border
+        cv2.rectangle(mosaic, (block_x, block_y), (block_x + w,
+                      block_y + h), (255, 255, 255), thickness=3)
+
+    if fname:
+        r = min(1280. / max(h, w) / ns, 1.0)  # ratio to limit image size
+        mosaic = cv2.resize(
+            mosaic, (int(ns * w * r), int(ns * h * r)), interpolation=cv2.INTER_AREA)
+        # cv2.imwrite(fname, cv2.cvtColor(mosaic, cv2.COLOR_BGR2RGB))  # cv2 save
+        Image.fromarray(mosaic).save(fname)  # PIL save
+    return mosaic
+
+
+def plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir=''):
+    # Plot LR simulating training for full epochs
+    optimizer, scheduler = copy(optimizer), copy(
+        scheduler)  # do not modify originals
+    y = []
+    for _ in range(epochs):
+        scheduler.step()
+        y.append(optimizer.param_groups[0]['lr'])
+    plt.plot(y, '.-', label='LR')
+    plt.xlabel('epoch')
+    plt.ylabel('LR')
+    plt.grid()
+    plt.xlim(0, epochs)
+    plt.ylim(0)
+    plt.tight_layout()
+    plt.savefig(Path(save_dir) / 'LR.png', dpi=200)
+
+
+def plot_test_txt():  # from utils.general import *; plot_test()
+    # Plot test.txt histograms
+    x = np.loadtxt('test.txt', dtype=np.float32)
+    box = xyxy2xywh(x[:, :4])
+    cx, cy = box[:, 0], box[:, 1]
+
+    fig, ax = plt.subplots(1, 1, figsize=(6, 6), tight_layout=True)
+    ax.hist2d(cx, cy, bins=600, cmax=10, cmin=0)
+    ax.set_aspect('equal')
+    plt.savefig('hist2d.png', dpi=300)
+
+    fig, ax = plt.subplots(1, 2, figsize=(12, 6), tight_layout=True)
+    ax[0].hist(cx, bins=600)
+    ax[1].hist(cy, bins=600)
+    plt.savefig('hist1d.png', dpi=200)
+
+
+def plot_targets_txt():  # from utils.general import *; plot_targets_txt()
+    # Plot targets.txt histograms
+    x = np.loadtxt('targets.txt', dtype=np.float32).T
+    s = ['x targets', 'y targets', 'width targets', 'height targets']
+    fig, ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)
+    ax = ax.ravel()
+    for i in range(4):
+        ax[i].hist(x[i], bins=100, label='%.3g +/- %.3g' %
+                   (x[i].mean(), x[i].std()))
+        ax[i].legend()
+        ax[i].set_title(s[i])
+    plt.savefig('targets.jpg', dpi=200)
+
+
+# from utils.general import *; plot_study_txt()
+def plot_study_txt(f='study.txt', x=None):
+    # Plot study.txt generated by test.py
+    fig, ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True)
+    ax = ax.ravel()
+
+    fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4), tight_layout=True)
+    for f in ['study/study_coco_yolo%s.txt' % x for x in ['s', 'm', 'l', 'x']]:
+        y = np.loadtxt(f, dtype=np.float32, usecols=[
+                       0, 1, 2, 3, 7, 8, 9], ndmin=2).T
+        x = np.arange(y.shape[1]) if x is None else np.array(x)
+        s = ['P', 'R', 'mAP@.5', 'mAP@.5:.95',
+             't_inference (ms/img)', 't_NMS (ms/img)', 't_total (ms/img)']
+        for i in range(7):
+            ax[i].plot(x, y[i], '.-', linewidth=2, markersize=8)
+            ax[i].set_title(s[i])
+
+        j = y[3].argmax() + 1
+        ax2.plot(y[6, :j], y[3, :j] * 1E2, '.-', linewidth=2, markersize=8,
+                 label=Path(f).stem.replace('study_coco_', '').replace('yolo', 'YOLO'))
+
+    ax2.plot(1E3 / np.array([209, 140, 97, 58, 35, 18]), [34.6, 40.5, 43.0, 47.5, 49.7, 51.5],
+             'k.-', linewidth=2, markersize=8, alpha=.25, label='EfficientDet')
+
+    ax2.grid()
+    ax2.set_xlim(0, 30)
+    ax2.set_ylim(28, 50)
+    ax2.set_yticks(np.arange(30, 55, 5))
+    ax2.set_xlabel('GPU Speed (ms/img)')
+    ax2.set_ylabel('COCO AP val')
+    ax2.legend(loc='lower right')
+    plt.savefig('study_mAP_latency.png', dpi=300)
+    plt.savefig(f.replace('.txt', '.png'), dpi=300)
+
+
+def plot_labels(labels, save_dir=''):
+    # plot dataset labels
+    c, b = labels[:, 0], labels[:, 1:].transpose()  # classes, boxes
+    nc = int(c.max() + 1)  # number of classes
+
+    fig, ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)
+    ax = ax.ravel()
+    ax[0].hist(c, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8)
+    ax[0].set_xlabel('classes')
+    ax[1].scatter(b[0], b[1], c=hist2d(b[0], b[1], 90), cmap='jet')
+    ax[1].set_xlabel('x')
+    ax[1].set_ylabel('y')
+    ax[2].scatter(b[2], b[3], c=hist2d(b[2], b[3], 90), cmap='jet')
+    ax[2].set_xlabel('width')
+    ax[2].set_ylabel('height')
+    plt.savefig(Path(save_dir) / 'labels.png', dpi=200)
+    plt.close()
+
+    # seaborn correlogram
+    try:
+        import seaborn as sns
+        import pandas as pd
+        x = pd.DataFrame(b.transpose(), columns=['x', 'y', 'width', 'height'])
+        sns.pairplot(x, corner=True, diag_kind='hist', kind='scatter', markers='o',
+                     plot_kws=dict(s=3, edgecolor=None,
+                                   linewidth=1, alpha=0.02),
+                     diag_kws=dict(bins=50))
+        plt.savefig(Path(save_dir) / 'labels_correlogram.png', dpi=200)
+        plt.close()
+    except Exception as e:
+        pass
+
+
+# from utils.general import *; plot_evolution()
+def plot_evolution(yaml_file='data/hyp.finetune.yaml'):
+    # Plot hyperparameter evolution results in evolve.txt
+    with open(yaml_file) as f:
+        hyp = yaml.load(f, Loader=yaml.FullLoader)
+    x = np.loadtxt('evolve.txt', ndmin=2)
+    f = fitness(x)
+    # weights = (f - f.min()) ** 2  # for weighted results
+    plt.figure(figsize=(10, 12), tight_layout=True)
+    matplotlib.rc('font', **{'size': 8})
+    for i, (k, v) in enumerate(hyp.items()):
+        y = x[:, i + 7]
+        # mu = (y * weights).sum() / weights.sum()  # best weighted result
+        mu = y[f.argmax()]  # best single result
+        plt.subplot(6, 5, i + 1)
+        plt.scatter(y, f, c=hist2d(y, f, 20), cmap='viridis',
+                    alpha=.8, edgecolors='none')
+        plt.plot(mu, f.max(), 'k+', markersize=15)
+        plt.title('%s = %.3g' % (k, mu), fontdict={
+                  'size': 9})  # limit to 40 characters
+        if i % 5 != 0:
+            plt.yticks([])
+        print('%15s: %.3g' % (k, mu))
+    plt.savefig('evolve.png', dpi=200)
+    print('\nPlot saved as evolve.png')
+
+
+# from utils.general import *; plot_results_overlay()
+def plot_results_overlay(start=0, stop=0):
+    # Plot training 'results*.txt', overlaying train and val losses
+    s = ['train', 'train', 'train', 'Precision', 'mAP@0.5',
+         'val', 'val', 'val', 'Recall', 'mAP@0.5:0.95']  # legends
+    t = ['Box', 'Objectness', 'Classification', 'P-R', 'mAP-F1']  # titles
+    for f in sorted(glob.glob('results*.txt') + glob.glob('../../Downloads/results*.txt')):
+        results = np.loadtxt(
+            f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T
+        n = results.shape[1]  # number of rows
+        x = range(start, min(stop, n) if stop else n)
+        fig, ax = plt.subplots(1, 5, figsize=(14, 3.5), tight_layout=True)
+        ax = ax.ravel()
+        for i in range(5):
+            for j in [i, i + 5]:
+                y = results[j, x]
+                ax[i].plot(x, y, marker='.', label=s[j])
+                # y_smooth = butter_lowpass_filtfilt(y)
+                # ax[i].plot(x, np.gradient(y_smooth), marker='.', label=s[j])
+
+            ax[i].set_title(t[i])
+            ax[i].legend()
+            ax[i].set_ylabel(f) if i == 0 else None  # add filename
+        fig.savefig(f.replace('.txt', '.png'), dpi=200)
+
+
+def plot_results(start=0, stop=0, bucket='', id=(), labels=(), save_dir=''):
+    # from utils.general import *; plot_results(save_dir='runs/train/exp0')
+    # Plot training 'results*.txt'
+    fig, ax = plt.subplots(2, 5, figsize=(12, 6))
+    ax = ax.ravel()
+    s = ['Box', 'Objectness', 'Classification', 'Precision', 'Recall',
+         'val Box', 'val Objectness', 'val Classification', 'mAP@0.5', 'mAP@0.5:0.95']
+    if bucket:
+        # os.system('rm -rf storage.googleapis.com')
+        # files = ['https://storage.googleapis.com/%s/results%g.txt' % (bucket, x) for x in id]
+        files = ['%g.txt' % x for x in id]
+        c = ('gsutil cp ' + '%s ' * len(files) +
+             '.') % tuple('gs://%s/%g.txt' % (bucket, x) for x in id)
+        os.system(c)
+    else:
+        files = glob.glob(str(Path(save_dir) / '*.txt')) + \
+            glob.glob('../../Downloads/results*.txt')
+    assert len(
+        files), 'No results.txt files found in %s, nothing to plot.' % os.path.abspath(save_dir)
+    for fi, f in enumerate(files):
+        try:
+            results = np.loadtxt(
+                f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T
+            n = results.shape[1]  # number of rows
+            x = range(start, min(stop, n) if stop else n)
+            for i in range(10):
+                y = results[i, x]
+                if i in [0, 1, 2, 5, 6, 7]:
+                    y[y == 0] = np.nan  # don't show zero loss values
+                    # y /= y[0]  # normalize
+                label = labels[fi] if len(labels) else Path(f).stem
+                ax[i].plot(x, y, marker='.', label=label,
+                           linewidth=1, markersize=6)
+                ax[i].set_title(s[i])
+                # if i in [5, 6, 7]:  # share train and val loss y axes
+                #     ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])
+        except Exception as e:
+            print('Warning: Plotting error for %s; %s' % (f, e))
+
+    fig.tight_layout()
+    ax[1].legend()
+    fig.savefig(Path(save_dir) / 'results.png', dpi=200)
diff --git a/model_image/yolor/utils/torch_utils.py b/model_image/yolor/utils/torch_utils.py
new file mode 100644
index 0000000..4d07baa
--- /dev/null
+++ b/model_image/yolor/utils/torch_utils.py
@@ -0,0 +1,240 @@
+# PyTorch utils
+
+import logging
+import math
+import os
+import time
+from contextlib import contextmanager
+from copy import deepcopy
+
+import torch
+import torch.backends.cudnn as cudnn
+import torch.nn as nn
+import torch.nn.functional as F
+import torchvision
+
+logger = logging.getLogger(__name__)
+
+
+@contextmanager
+def torch_distributed_zero_first(local_rank: int):
+    """
+    Decorator to make all processes in distributed training wait for each local_master to do something.
+    """
+    if local_rank not in [-1, 0]:
+        torch.distributed.barrier()
+    yield
+    if local_rank == 0:
+        torch.distributed.barrier()
+
+
+def init_torch_seeds(seed=0):
+    # Speed-reproducibility tradeoff https://pytorch.org/docs/stable/notes/randomness.html
+    torch.manual_seed(seed)
+    if seed == 0:  # slower, more reproducible
+        cudnn.deterministic = True
+        cudnn.benchmark = False
+    else:  # faster, less reproducible
+        cudnn.deterministic = False
+        cudnn.benchmark = True
+
+
+def select_device(device='', batch_size=None):
+    # device = 'cpu' or '0' or '0,1,2,3'
+    cpu_request = device.lower() == 'cpu'
+    if device and not cpu_request:  # if device requested other than 'cpu'
+        os.environ['CUDA_VISIBLE_DEVICES'] = device  # set environment variable
+        assert torch.cuda.is_available(), 'CUDA unavailable, invalid device %s requested' % device  # check availablity
+
+    cuda = False if cpu_request else torch.cuda.is_available()
+    if cuda:
+        c = 1024 ** 2  # bytes to MB
+        ng = torch.cuda.device_count()
+        if ng > 1 and batch_size:  # check that batch_size is compatible with device_count
+            assert batch_size % ng == 0, 'batch-size %g not multiple of GPU count %g' % (batch_size, ng)
+        x = [torch.cuda.get_device_properties(i) for i in range(ng)]
+        s = f'Using torch {torch.__version__} '
+        for i in range(0, ng):
+            if i == 1:
+                s = ' ' * len(s)
+            logger.info("%sCUDA:%g (%s, %dMB)" % (s, i, x[i].name, x[i].total_memory / c))
+    else:
+        logger.info(f'Using torch {torch.__version__} CPU')
+
+    logger.info('')  # skip a line
+    return torch.device('cuda:0' if cuda else 'cpu')
+
+
+def time_synchronized():
+    torch.cuda.synchronize() if torch.cuda.is_available() else None
+    return time.time()
+
+
+def is_parallel(model):
+    return type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel)
+
+
+def intersect_dicts(da, db, exclude=()):
+    # Dictionary intersection of matching keys and shapes, omitting 'exclude' keys, using da values
+    return {k: v for k, v in da.items() if k in db and not any(x in k for x in exclude) and v.shape == db[k].shape}
+
+
+def initialize_weights(model):
+    for m in model.modules():
+        t = type(m)
+        if t is nn.Conv2d:
+            pass  # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+        elif t is nn.BatchNorm2d:
+            m.eps = 1e-3
+            m.momentum = 0.03
+        elif t in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6]:
+            m.inplace = True
+
+
+def find_modules(model, mclass=nn.Conv2d):
+    # Finds layer indices matching module class 'mclass'
+    return [i for i, m in enumerate(model.module_list) if isinstance(m, mclass)]
+
+
+def sparsity(model):
+    # Return global model sparsity
+    a, b = 0., 0.
+    for p in model.parameters():
+        a += p.numel()
+        b += (p == 0).sum()
+    return b / a
+
+
+def prune(model, amount=0.3):
+    # Prune model to requested global sparsity
+    import torch.nn.utils.prune as prune
+    print('Pruning model... ', end='')
+    for name, m in model.named_modules():
+        if isinstance(m, nn.Conv2d):
+            prune.l1_unstructured(m, name='weight', amount=amount)  # prune
+            prune.remove(m, 'weight')  # make permanent
+    print(' %.3g global sparsity' % sparsity(model))
+
+
+def fuse_conv_and_bn(conv, bn):
+    # Fuse convolution and batchnorm layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/
+    fusedconv = nn.Conv2d(conv.in_channels,
+                          conv.out_channels,
+                          kernel_size=conv.kernel_size,
+                          stride=conv.stride,
+                          padding=conv.padding,
+                          groups=conv.groups,
+                          bias=True).requires_grad_(False).to(conv.weight.device)
+
+    # prepare filters
+    w_conv = conv.weight.clone().view(conv.out_channels, -1)
+    w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var)))
+    fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.size()))
+
+    # prepare spatial bias
+    b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) if conv.bias is None else conv.bias
+    b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps))
+    fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn)
+
+    return fusedconv
+
+
+def model_info(model, verbose=False, img_size=640):
+    # Model information. img_size may be int or list, i.e. img_size=640 or img_size=[640, 320]
+    n_p = sum(x.numel() for x in model.parameters())  # number parameters
+    n_g = sum(x.numel() for x in model.parameters() if x.requires_grad)  # number gradients
+    if verbose:
+        print('%5s %40s %9s %12s %20s %10s %10s' % ('layer', 'name', 'gradient', 'parameters', 'shape', 'mu', 'sigma'))
+        for i, (name, p) in enumerate(model.named_parameters()):
+            name = name.replace('module_list.', '')
+            print('%5g %40s %9s %12g %20s %10.3g %10.3g' %
+                  (i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std()))
+
+    try:  # FLOPS
+        from thop import profile
+        flops = profile(deepcopy(model), inputs=(torch.zeros(1, 3, img_size, img_size),), verbose=False)[0] / 1E9 * 2
+        img_size = img_size if isinstance(img_size, list) else [img_size, img_size]  # expand if int/float
+        fs = ', %.9f GFLOPS' % (flops)  # 640x640 FLOPS
+    except (ImportError, Exception):
+        fs = ''
+
+    logger.info(f"Model Summary: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}")
+
+
+def load_classifier(name='resnet101', n=2):
+    # Loads a pretrained model reshaped to n-class output
+    model = torchvision.models.__dict__[name](pretrained=True)
+
+    # ResNet model properties
+    # input_size = [3, 224, 224]
+    # input_space = 'RGB'
+    # input_range = [0, 1]
+    # mean = [0.485, 0.456, 0.406]
+    # std = [0.229, 0.224, 0.225]
+
+    # Reshape output to n classes
+    filters = model.fc.weight.shape[1]
+    model.fc.bias = nn.Parameter(torch.zeros(n), requires_grad=True)
+    model.fc.weight = nn.Parameter(torch.zeros(n, filters), requires_grad=True)
+    model.fc.out_features = n
+    return model
+
+
+def scale_img(img, ratio=1.0, same_shape=False):  # img(16,3,256,416), r=ratio
+    # scales img(bs,3,y,x) by ratio
+    if ratio == 1.0:
+        return img
+    else:
+        h, w = img.shape[2:]
+        s = (int(h * ratio), int(w * ratio))  # new size
+        img = F.interpolate(img, size=s, mode='bilinear', align_corners=False)  # resize
+        if not same_shape:  # pad/crop img
+            gs = 32  # (pixels) grid size
+            h, w = [math.ceil(x * ratio / gs) * gs for x in (h, w)]
+        return F.pad(img, [0, w - s[1], 0, h - s[0]], value=0.447)  # value = imagenet mean
+
+
+def copy_attr(a, b, include=(), exclude=()):
+    # Copy attributes from b to a, options to only include [...] and to exclude [...]
+    for k, v in b.__dict__.items():
+        if (len(include) and k not in include) or k.startswith('_') or k in exclude:
+            continue
+        else:
+            setattr(a, k, v)
+
+
+class ModelEMA:
+    """ Model Exponential Moving Average from https://github.com/rwightman/pytorch-image-models
+    Keep a moving average of everything in the model state_dict (parameters and buffers).
+    This is intended to allow functionality like
+    https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage
+    A smoothed version of the weights is necessary for some training schemes to perform well.
+    This class is sensitive where it is initialized in the sequence of model init,
+    GPU assignment and distributed training wrappers.
+    """
+
+    def __init__(self, model, decay=0.9999, updates=0):
+        # Create EMA
+        self.ema = deepcopy(model.module if is_parallel(model) else model).eval()  # FP32 EMA
+        # if next(model.parameters()).device.type != 'cpu':
+        #     self.ema.half()  # FP16 EMA
+        self.updates = updates  # number of EMA updates
+        self.decay = lambda x: decay * (1 - math.exp(-x / 2000))  # decay exponential ramp (to help early epochs)
+        for p in self.ema.parameters():
+            p.requires_grad_(False)
+
+    def update(self, model):
+        # Update EMA parameters
+        with torch.no_grad():
+            self.updates += 1
+            d = self.decay(self.updates)
+
+            msd = model.module.state_dict() if is_parallel(model) else model.state_dict()  # model state_dict
+            for k, v in self.ema.state_dict().items():
+                if v.dtype.is_floating_point:
+                    v *= d
+                    v += (1. - d) * msd[k].detach()
+
+    def update_attr(self, model, include=(), exclude=('process_group', 'reducer')):
+        # Update EMA attributes
+        copy_attr(self.ema, model, include, exclude)
diff --git a/run.py b/run.py
index 4b67b6a..f90ee16 100644
--- a/run.py
+++ b/run.py
@@ -1,6 +1,5 @@
-from min_max_utils.HTTPLIB2Capture import HTTPLIB2Capture
 from logging import Logger
-from get_predictions import predict_boxes, predict_human, predict_bottles
+from connection import HTTPLIB2Capture, ModelPredictionsReceiver
 from min_max_utils.min_max_utils import filter_boxes, check_box_in_area, convert_coords_from_dict_to_list, drop_area, \
     most_common
 from confs.load_configs import N_STEPS
@@ -10,6 +9,8 @@
 
 def run_min_max(dataset: HTTPLIB2Capture, logger: Logger, areas: list[dict],
                 folder: str, debug_folder: str, server_url: str, zones: list):
+    
+    model_pred_receiver = ModelPredictionsReceiver(server_url, logger)
     stat_history = []
     is_human_was_detected = True
     n_iters = 0
@@ -24,14 +25,14 @@ def run_min_max(dataset: HTTPLIB2Capture, logger: Logger, areas: list[dict],
         if n_iters % 60 == 0:
             logger.debug("60 detect iterations passed")
 
-        human_preds = predict_human(img.copy(), server_url, logger)
-        if human_preds[0] is None:
-            time.sleep(1)
-            continue
-        is_human_in_image_now = human_preds[0] != 0
+        human_preds = model_pred_receiver.predict_human(img.copy())
+
+        is_human_in_image_now = human_preds is not None and human_preds.size 
 
         if is_human_in_image_now:
             logger.debug("Human is detected")
+            time.sleep(1)
+            continue
 
         if (is_human_was_detected and not is_human_in_image_now) or \
                 (not is_human_was_detected and not is_human_in_image_now and
@@ -42,10 +43,10 @@ def run_min_max(dataset: HTTPLIB2Capture, logger: Logger, areas: list[dict],
                 for zone in zones:
                     x1, y1, x2, y2 = convert_coords_from_dict_to_list(zone.get("coords")[0])
                     cropped_images.append(img[y1:y2, x1:x2])    
-                model_preds_boxes = [predict_boxes(crop_img, server_url, logger) for crop_img in cropped_images]
+                model_preds_boxes = [model_pred_receiver.predict_boxes(crop_img) for crop_img in cropped_images]
                 if any(elem is None for elem in model_preds_boxes):
                     continue
-                model_preds_bottles = [predict_bottles(crop_img, server_url, logger) for crop_img in cropped_images]
+                model_preds_bottles = [model_pred_receiver.predict_bottles(crop_img) for crop_img in cropped_images]
                 if any(elem[0] is None for elem in model_preds_bottles):
                     continue
 
@@ -85,13 +86,12 @@ def run_min_max(dataset: HTTPLIB2Capture, logger: Logger, areas: list[dict],
                             logger.critical("Area is not in zone")
                             exit(1)
                     else:
-                        send_request_func = predict_boxes if item["task"] == "boxes" else predict_bottles
-                        model_preds = send_request_func(img[area_coord[1]:area_coord[3], area_coord[0]:area_coord[2]],
-                                                    server_url, logger)
-                        if model_preds[0] is None:
+                        send_request_func = model_pred_receiver.predict_boxes if item["task"] == "boxes" else model_pred_receiver.predict_bottles
+                        model_preds = send_request_func(img[area_coord[1]:area_coord[3], area_coord[0]:area_coord[2]])
+                        if model_preds is None:
                             time.sleep(1)
                             break
-                        boxes_preds = filter_boxes(area_coord, *model_preds, check=False)
+                        boxes_preds = filter_boxes(area_coord, model_preds, check=False)
                     item_stat.append(boxes_preds)
             if item_stat:
                 areas_stat.append(item_stat)