Reproduce by `python val.py -data coco.yaml -img 1536 -iou 0.7 -augment` **TTA** () includes reflection and scale augmentations. Reproduce by `python val.py -data coco.yaml -img 640 -task speed -batch 1` **Speed** averaged over COCO val images using a () instance. Reproduce by `python val.py -data coco.yaml -img 640 -conf 0.001 -iou 0.65` **mAP val** values are for single-model single-scale on () dataset. Nano and Small models use () hyps, all others use (). All checkpoints are trained to 300 epochs with default settings. Please browse the YOLOv5 Docs for details, raise an issue on GitHub for support, and join our Discord community for questions and discussions! Model We hope that the resources here will help you get the most out of YOLOv5. YOLOv5 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection, instance segmentation and image classification tasks. Ultralytics YOLOv5 □ is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces new features and improvements to further boost performance and flexibility.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |