diff --git a/README.md b/README.md
index 00debfaf8..f97c7035c 100644
--- a/README.md
+++ b/README.md
@@ -50,18 +50,18 @@ retinanet-train coco /path/to/MS/COCO
 The pretrained MS COCO model can be downloaded [here](https://github.com/fizyr/keras-retinanet/releases/download/0.1/resnet50_coco_best_v1.2.2.h5). Results using the `cocoapi` are shown below (note: according to the paper, this configuration should achieve a mAP of 0.343).
 
 ```
- Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.325
- Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.513
- Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.342
- Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.149
- Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.354
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.345
+ Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.533
+ Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.368
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.189
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.380
  Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.465
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.288
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.437
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.464
- Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.263
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.510
- Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.623
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.301
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.482
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.529
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.364
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.565
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.666
 ```
 
 For training on [OID](https://github.com/openimages/dataset), run:
@@ -118,7 +118,7 @@ from keras_retinanet.models.resnet import custom_objects
 model = keras.models.load_model('/path/to/model.h5', custom_objects=custom_objects)
 ```
 
-Execution time on NVIDIA Pascal Titan X is roughly 75msec for an image of shape `1000x600x3`.
+Execution time on NVIDIA Pascal Titan X is roughly 75msec for an image of shape `1000x800x3`.
 
 ## CSV datasets
 The `CSVGenerator` provides an easy way to define your own datasets.