v3.0
This releases includes nn.Hardswish() activation implementation on Conv() modules, which increases mAP for all models at the expense of about 10% in inference speed. Training speeds are not significantly affected, though CUDA memory requirements increase about 10%. Training from scratch as well as finetuning both benefit from this change. The smallest models benefit the most from the Hardswish() activations, with increases of +0.9/+0.8/+0.7/+0.2mAP@0.5:0.95 for YOLOv5s/m/l/x.
All mAP values in our README are now reported at --img-size 640 (v2.0 reported at 672, and v1.0 reported at 736), so we've succeeded in increasing mAP while reducing the required --img-size :)
We've also listed YOLOv5x Test Time Augmentation (TTA) mAP and speeds for v3.0 in our README table for the first time (and for v2.0 below). Best results are YOLOv5x with TTA at 50.8 mAP@0.5:0.95. We've also updated efficientdet results in our comparison plot to reflect recent improvements in the google/automl repo.
Breaking Changes
- This release does not contain breaking changes.
- This release is only backwards compatible with v2.0 models trained with torch>=1.6.
Bug Fixes
- Hyperparameter evolution fixed, tutorial added (https://github.com/ultralytics/yolov5/issues/607)
