Open glenn-jocher opened 4 years ago
Is this repository going to be archived or will yolov3 and yolov5 development fork? it looks like you support yolov3-spp in the yolov5 repo anyway.
@pfeatherstone all future development is focused on https://github.com/ultralytics/yolov5
This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.
This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.
This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.
This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.
Ultralytics has open-sourced YOLOv5 at https://github.com/ultralytics/yolov5, featuring faster, lighter and more accurate object detection. YOLOv5 is recommended for all new projects.
Pretrained Checkpoints
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
For more information and to get started with YOLOv5 please visit https://github.com/ultralytics/yolov5. Thank you!