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Merge pull request #9 from ucb-bar/yolo
ADD: add YOLO v8 example
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| # YOLO v8 | ||
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| ## Model Architecture | ||
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| ```python | ||
| YOLO( | ||
| (model): DetectionModel( | ||
| (model): Sequential( | ||
| (0): Conv( | ||
| (conv): Conv2d(3, 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) | ||
| (bn): BatchNorm2d(16, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) | ||
| (act): SiLU(inplace=True) | ||
| ) | ||
| (1): Conv( | ||
| (conv): Conv2d(16, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) | ||
| (bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) | ||
| (act): SiLU(inplace=True) | ||
| ) | ||
| (2): C2f( | ||
| (cv1): Conv( | ||
| (conv): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1), bias=False) | ||
| (bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) | ||
| (act): SiLU(inplace=True) | ||
| ) | ||
| (cv2): Conv( | ||
| (conv): Conv2d(48, 32, kernel_size=(1, 1), stride=(1, 1), bias=False) | ||
| (bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) | ||
| (act): SiLU(inplace=True) | ||
| ) | ||
| (m): ModuleList( | ||
| (0): Bottleneck( | ||
| (cv1): Conv( | ||
| (conv): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | ||
| (bn): BatchNorm2d(16, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) | ||
| (act): SiLU(inplace=True) | ||
| ) | ||
| (cv2): Conv( | ||
| (conv): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | ||
| (bn): BatchNorm2d(16, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) | ||
| (act): SiLU(inplace=True) | ||
| ) | ||
| ) | ||
| ) | ||
| ) | ||
| (3): Conv( | ||
| (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) | ||
| (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) | ||
| (act): SiLU(inplace=True) | ||
| ) | ||
| (4): C2f( | ||
| (cv1): Conv( | ||
| (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) | ||
| (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) | ||
| (act): SiLU(inplace=True) | ||
| ) | ||
| (cv2): Conv( | ||
| (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) | ||
| (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) | ||
| (act): SiLU(inplace=True) | ||
| ) | ||
| (m): ModuleList( | ||
| (0-1): 2 x Bottleneck( | ||
| (cv1): Conv( | ||
| (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | ||
| (bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) | ||
| (act): SiLU(inplace=True) | ||
| ) | ||
| (cv2): Conv( | ||
| (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | ||
| (bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) | ||
| (act): SiLU(inplace=True) | ||
| ) | ||
| ) | ||
| ) | ||
| ) | ||
| (5): Conv( | ||
| (conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) | ||
| (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) | ||
| (act): SiLU(inplace=True) | ||
| ) | ||
| (6): C2f( | ||
| (cv1): Conv( | ||
| (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) | ||
| (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) | ||
| (act): SiLU(inplace=True) | ||
| ) | ||
| (cv2): Conv( | ||
| (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) | ||
| (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) | ||
| (act): SiLU(inplace=True) | ||
| ) | ||
| (m): ModuleList( | ||
| (0-1): 2 x Bottleneck( | ||
| (cv1): Conv( | ||
| (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | ||
| (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) | ||
| (act): SiLU(inplace=True) | ||
| ) | ||
| (cv2): Conv( | ||
| (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | ||
| (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) | ||
| (act): SiLU(inplace=True) | ||
| ) | ||
| ) | ||
| ) | ||
| ) | ||
| (7): Conv( | ||
| (conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) | ||
| (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) | ||
| (act): SiLU(inplace=True) | ||
| ) | ||
| (8): C2f( | ||
| (cv1): Conv( | ||
| (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) | ||
| (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) | ||
| (act): SiLU(inplace=True) | ||
| ) | ||
| (cv2): Conv( | ||
| (conv): Conv2d(384, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) | ||
| (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) | ||
| (act): SiLU(inplace=True) | ||
| ) | ||
| (m): ModuleList( | ||
| (0): Bottleneck( | ||
| (cv1): Conv( | ||
| (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | ||
| (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) | ||
| (act): SiLU(inplace=True) | ||
| ) | ||
| (cv2): Conv( | ||
| (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | ||
| (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) | ||
| (act): SiLU(inplace=True) | ||
| ) | ||
| ) | ||
| ) | ||
| ) | ||
| (9): SPPF( | ||
| (cv1): Conv( | ||
| (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) | ||
| (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) | ||
| (act): SiLU(inplace=True) | ||
| ) | ||
| (cv2): Conv( | ||
| (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) | ||
| (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) | ||
| (act): SiLU(inplace=True) | ||
| ) | ||
| (m): MaxPool2d(kernel_size=5, stride=1, padding=2, dilation=1, ceil_mode=False) | ||
| ) | ||
| (10): Upsample(scale_factor=2.0, mode='nearest') | ||
| (11): Concat() | ||
| (12): C2f( | ||
| (cv1): Conv( | ||
| (conv): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) | ||
| (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) | ||
| (act): SiLU(inplace=True) | ||
| ) | ||
| (cv2): Conv( | ||
| (conv): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) | ||
| (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) | ||
| (act): SiLU(inplace=True) | ||
| ) | ||
| (m): ModuleList( | ||
| (0): Bottleneck( | ||
| (cv1): Conv( | ||
| (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | ||
| (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) | ||
| (act): SiLU(inplace=True) | ||
| ) | ||
| (cv2): Conv( | ||
| (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | ||
| (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) | ||
| (act): SiLU(inplace=True) | ||
| ) | ||
| ) | ||
| ) | ||
| ) | ||
| (13): Upsample(scale_factor=2.0, mode='nearest') | ||
| (14): Concat() | ||
| (15): C2f( | ||
| (cv1): Conv( | ||
| (conv): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) | ||
| (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) | ||
| (act): SiLU(inplace=True) | ||
| ) | ||
| (cv2): Conv( | ||
| (conv): Conv2d(96, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) | ||
| (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) | ||
| (act): SiLU(inplace=True) | ||
| ) | ||
| (m): ModuleList( | ||
| (0): Bottleneck( | ||
| (cv1): Conv( | ||
| (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | ||
| (bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) | ||
| (act): SiLU(inplace=True) | ||
| ) | ||
| (cv2): Conv( | ||
| (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | ||
| (bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) | ||
| (act): SiLU(inplace=True) | ||
| ) | ||
| ) | ||
| ) | ||
| ) | ||
| (16): Conv( | ||
| (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) | ||
| (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) | ||
| (act): SiLU(inplace=True) | ||
| ) | ||
| (17): Concat() | ||
| (18): C2f( | ||
| (cv1): Conv( | ||
| (conv): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) | ||
| (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) | ||
| (act): SiLU(inplace=True) | ||
| ) | ||
| (cv2): Conv( | ||
| (conv): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) | ||
| (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) | ||
| (act): SiLU(inplace=True) | ||
| ) | ||
| (m): ModuleList( | ||
| (0): Bottleneck( | ||
| (cv1): Conv( | ||
| (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | ||
| (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) | ||
| (act): SiLU(inplace=True) | ||
| ) | ||
| (cv2): Conv( | ||
| (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | ||
| (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) | ||
| (act): SiLU(inplace=True) | ||
| ) | ||
| ) | ||
| ) | ||
| ) | ||
| (19): Conv( | ||
| (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) | ||
| (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) | ||
| (act): SiLU(inplace=True) | ||
| ) | ||
| (20): Concat() | ||
| (21): C2f( | ||
| (cv1): Conv( | ||
| (conv): Conv2d(384, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) | ||
| (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) | ||
| (act): SiLU(inplace=True) | ||
| ) | ||
| (cv2): Conv( | ||
| (conv): Conv2d(384, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) | ||
| (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) | ||
| (act): SiLU(inplace=True) | ||
| ) | ||
| (m): ModuleList( | ||
| (0): Bottleneck( | ||
| (cv1): Conv( | ||
| (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | ||
| (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) | ||
| (act): SiLU(inplace=True) | ||
| ) | ||
| (cv2): Conv( | ||
| (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | ||
| (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) | ||
| (act): SiLU(inplace=True) | ||
| ) | ||
| ) | ||
| ) | ||
| ) | ||
| (22): Detect( | ||
| (cv2): ModuleList( | ||
| (0): Sequential( | ||
| (0): Conv( | ||
| (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | ||
| (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) | ||
| (act): SiLU(inplace=True) | ||
| ) | ||
| (1): Conv( | ||
| (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | ||
| (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) | ||
| (act): SiLU(inplace=True) | ||
| ) | ||
| (2): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1)) | ||
| ) | ||
| (1): Sequential( | ||
| (0): Conv( | ||
| (conv): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | ||
| (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) | ||
| (act): SiLU(inplace=True) | ||
| ) | ||
| (1): Conv( | ||
| (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | ||
| (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) | ||
| (act): SiLU(inplace=True) | ||
| ) | ||
| (2): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1)) | ||
| ) | ||
| (2): Sequential( | ||
| (0): Conv( | ||
| (conv): Conv2d(256, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | ||
| (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) | ||
| (act): SiLU(inplace=True) | ||
| ) | ||
| (1): Conv( | ||
| (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | ||
| (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) | ||
| (act): SiLU(inplace=True) | ||
| ) | ||
| (2): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1)) | ||
| ) | ||
| ) | ||
| (cv3): ModuleList( | ||
| (0): Sequential( | ||
| (0): Conv( | ||
| (conv): Conv2d(64, 80, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | ||
| (bn): BatchNorm2d(80, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) | ||
| (act): SiLU(inplace=True) | ||
| ) | ||
| (1): Conv( | ||
| (conv): Conv2d(80, 80, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | ||
| (bn): BatchNorm2d(80, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) | ||
| (act): SiLU(inplace=True) | ||
| ) | ||
| (2): Conv2d(80, 80, kernel_size=(1, 1), stride=(1, 1)) | ||
| ) | ||
| (1): Sequential( | ||
| (0): Conv( | ||
| (conv): Conv2d(128, 80, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | ||
| (bn): BatchNorm2d(80, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) | ||
| (act): SiLU(inplace=True) | ||
| ) | ||
| (1): Conv( | ||
| (conv): Conv2d(80, 80, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | ||
| (bn): BatchNorm2d(80, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) | ||
| (act): SiLU(inplace=True) | ||
| ) | ||
| (2): Conv2d(80, 80, kernel_size=(1, 1), stride=(1, 1)) | ||
| ) | ||
| (2): Sequential( | ||
| (0): Conv( | ||
| (conv): Conv2d(256, 80, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | ||
| (bn): BatchNorm2d(80, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) | ||
| (act): SiLU(inplace=True) | ||
| ) | ||
| (1): Conv( | ||
| (conv): Conv2d(80, 80, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | ||
| (bn): BatchNorm2d(80, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) | ||
| (act): SiLU(inplace=True) | ||
| ) | ||
| (2): Conv2d(80, 80, kernel_size=(1, 1), stride=(1, 1)) | ||
| ) | ||
| ) | ||
| (dfl): DFL( | ||
| (conv): Conv2d(16, 1, kernel_size=(1, 1), stride=(1, 1), bias=False) | ||
| ) | ||
| ) | ||
| ) | ||
| ) | ||
| ) | ||
|
|
||
| ``` |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,15 @@ | ||
| import torch | ||
| from ultralytics import YOLO | ||
|
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| # Load a model | ||
| model = YOLO("yolov8n.yaml") # build a new model from scratch | ||
| model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training) | ||
|
|
||
| breakpoint() | ||
|
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| # Use the model | ||
| model.train(data="coco128.yaml", epochs=3) # train the model | ||
| metrics = model.val() # evaluate model performance on the validation set | ||
| results = model("https://ultralytics.com/images/bus.jpg") # predict on an image | ||
|
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||
| print(results) |