v2.0 Release (#491)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

README.md

@@ -8,26 +8,28 @@ This repository represents Ultralytics open-source research into future object d
 
 <img src="https://user-images.githubusercontent.com/26833433/85340570-30360a80-b49b-11ea-87cf-bdf33d53ae15.png" width="1000">** GPU Speed measures end-to-end time per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size 8, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS.
 
+- **July 23, 2020**: [v2.0 release](https://arxiv.org/abs/1803.01534): improved model definition, training and mAP []().
 - **June 22, 2020**: [PANet](https://arxiv.org/abs/1803.01534) updates: new heads, reduced parameters, faster inference and improved mAP [364fcfd](https://github.com/ultralytics/yolov5/commit/364fcfd7dba53f46edd4f04c037a039c0a287972).
 - **June 19, 2020**: [FP16](https://pytorch.org/docs/stable/nn.html#torch.nn.Module.half) as new default for smaller checkpoints and faster inference [d4c6674](https://github.com/ultralytics/yolov5/commit/d4c6674c98e19df4c40e33a777610a18d1961145).
 - **June 9, 2020**: [CSP](https://github.com/WongKinYiu/CrossStagePartialNetworks) updates: improved speed, size, and accuracy (credit to @WongKinYiu for CSP).
-- **May 27, 2020**: Public release of repo. YOLOv5 models are SOTA among all known YOLO implementations.
-- **April 1, 2020**: Start development of future [YOLOv3](https://github.com/ultralytics/yolov3)/[YOLOv4](https://github.com/AlexeyAB/darknet)-based PyTorch models in a range of compound-scaled sizes.
+- **May 27, 2020**: Public release. YOLOv5 models are SOTA among all known YOLO implementations.
+- **April 1, 2020**: Start development of future compound-scaled [YOLOv3](https://github.com/ultralytics/yolov3)/[YOLOv4](https://github.com/AlexeyAB/darknet)-based PyTorch models.
 
 
 ## Pretrained Checkpoints
 
 | Model | AP<sup>val</sup> | AP<sup>test</sup> | AP<sub>50</sub> | Speed<sub>GPU</sub> | FPS<sub>GPU</sub> || params | FLOPS |
 |---------- |------ |------ |------ | -------- | ------| ------ |------  |  :------: |
-| [YOLOv5s](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J)    | 36.6     | 36.6     | 55.8     | **2.1ms** | **476** || 7.5M   | 13.2B
-| [YOLOv5m](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J)    | 43.4     | 43.4     | 62.4     | 3.0ms     | 333     || 21.8M  | 39.4B
-| [YOLOv5l](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J)    | 46.6     | 46.7     | 65.4     | 3.9ms     | 256     || 47.8M  | 88.1B
-| [YOLOv5x](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J)    | **48.4** | **48.4** | **66.9** | 6.1ms     | 164     || 89.0M  | 166.4B
+| [YOLOv5.1s](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J)    | 36.1     | 36.1     | 55.3     | **2.1ms** | **476** || 7.5M   | 13.2B
+| [YOLOv5.1m](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J)    | 43.5     | 43.5     | 62.5     | 3.0ms     | 333     || 21.8M  | 39.4B
+| [YOLOv5.1l](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J)    | 47.0     | 47.1     | 65.6     | 3.9ms     | 256     || 47.8M  | 88.1B
+| [YOLOv5.1x](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J)    | **49.0** | **49.0** | **67.4** | 6.1ms     | 164     || 89.0M  | 166.4B
+| | | | | | || |
 | [YOLOv3-SPP](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J) | 45.6     | 45.5     | 65.2     | 4.5ms     | 222     || 63.0M  | 118.0B
 
 
 ** AP<sup>test</sup> denotes COCO [test-dev2017](http://cocodataset.org/#upload) server results, all other AP results in the table denote val2017 accuracy.  
-** All AP numbers are for single-model single-scale without ensemble or test-time augmentation. Reproduce by `python test.py --data coco.yaml --img 736 --conf 0.001`  
+** All AP numbers are for single-model single-scale without ensemble or test-time augmentation. Reproduce by `python test.py --data coco.yaml --img 672 --conf 0.001`  
 ** Speed<sub>GPU</sub> measures end-to-end time per image averaged over 5000 COCO val2017 images using a GCP [n1-standard-16](https://cloud.google.com/compute/docs/machine-types#n1_standard_machine_types) instance with one V100 GPU, and includes image preprocessing, PyTorch FP16 image inference at --batch-size 32 --img-size 640, postprocessing and NMS. Average NMS time included in this chart is 1-2ms/img.  Reproduce by `python test.py --data coco.yaml --img 640 --conf 0.1`  
 ** All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation).