Breaking Changes This release does not contain breaking changes. Bug Fixes Hyperparameter evolution fixed, tutorial added (https://github.com/ultralytics/yolov5/issues/607) Added Functionality PyTorch 1.6 native AMP replaces NVIDIA Apex AMP (https://github.com/ultralytics/yolov5/pull/573) nn.Hardswish() activations replace nn.LeakyReLU(0.1) in base convolution module models.Conv() Dataset Autodownload feature added (https://github.com/ultralytics/yolov5/pull/685) Model Autodownload improved (https://github.com/ultralytics/yolov5/pull/711) Layer freezing code added (https://github.com/ultralytics/yolov5/issues/679) TensorRT export tutorial added (https://github.com/ultralytics/yolov5/pull/623) <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. August 13, 2020: v3.0 release: nn.Hardswish() activations, data autodownload, native AMP. July 23, 2020: v2.0 release: improved model definition, training and mAP. June 22, 2020: PANet updates: new heads, reduced parameters, improved speed and mAP 364fcfd. June 19, 2020: FP16 as new default for smaller checkpoints and faster inference d4c6674. June 9, 2020: CSP updates: improved speed, size, and accuracy (credit to @WongKinYiu for CSP). May 27, 2020: Public release. YOLOv5 models are SOTA among all known YOLO implementations. April 1, 2020: Start development of future compound-scaled YOLOv3/YOLOv4-based PyTorch models. Pretrained Checkpoints v3.0 with nn.Hardswish() Model APval APtest AP50 SpeedGPU FPSGPU params FLOPS YOLOv5s 37.0 37.0 56.2 2.4ms 476 7.5M 13.2B YOLOv5m 44.3 44.3 63.2 3.4ms 333 21.8M 39.4B YOLOv5l 47.7 47.7 66.5 4.4ms 256 47.8M 88.1B YOLOv5x 49.2 49.2 67.7 6.9ms 164 89.0M 166.4B YOLOv5x + TTA 50.8 50.8 68.9 25.5ms 39 89.0M 354.3B YOLOv3-SPP 45.6 45.5 65.2 4.5ms 222 63.0M 118.0B APtest denotes COCO test-dev2017 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 640 --conf 0.001
SpeedGPU measures end-to-end time per image averaged over 5000 COCO val2017 images using a GCP n1-standard-16 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). Test Time Augmentation (TTA) runs at 3 image sizes. Reproduce** by python test.py --data coco.yaml --img 832 --augment v2.0 with nn.LeakyReLU(0.1) Model APval APtest AP50 SpeedGPU FPSGPU params FLOPS YOLOv5s 36.1 36.1 55.3 2.2ms 476 7.5M 13.2B YOLOv5m 43.5 43.5 62.5 3.2ms 333 21.8M 39.4B YOLOv5l 47.0 47.1 65.6 4.1ms 256 47.8M 88.1B YOLOv5x 49.0 49.0 67.4 6.4ms 164 89.0M 166.4B YOLOv5x + TTA 50.4 50.4 68.5 23.4ms 43 89.0M 354.3B YOLOv3-SPP 45.6 45.5 65.2 4.5ms 222 63.0M 118.0B APtest denotes COCO test-dev2017 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 672 --conf 0.001
SpeedGPU measures end-to-end time per image averaged over 5000 COCO val2017 images using a GCP n1-standard-16 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).

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