YOLOv5 is a family of object detection architectures and models pretrained on the COCO dataset, representing Ultralytics open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.
performance tested on Ascend 910(8p) with graph mode
| Name | Scale | BatchSize | ImageSize | Dataset | Box mAP (%) | Params | Recipe | Download |
|---|---|---|---|---|---|---|---|---|
| YOLOv5 | N | 32 * 8 | 640 | MS COCO 2017 | 27.3 | 1.9M | yaml | weights |
| YOLOv5 | S | 32 * 8 | 640 | MS COCO 2017 | 37.6 | 7.2M | yaml | weights |
| YOLOv5 | M | 32 * 8 | 640 | MS COCO 2017 | 44.9 | 21.2M | yaml | weights |
| YOLOv5 | L | 32 * 8 | 640 | MS COCO 2017 | 48.5 | 46.5M | yaml | weights |
| YOLOv5 | X | 16 * 8 | 640 | MS COCO 2017 | 50.5 | 86.7M | yaml | weights |
performance tested on Ascend 910*(8p)
| Name | Scale | BatchSize | ImageSize | Dataset | Box mAP (%) | ms/step | Params | Recipe | Download |
|---|---|---|---|---|---|---|---|---|---|
| YOLOv5 | N | 32 * 8 | 640 | MS COCO 2017 | 27.4 | 736.08 | 1.9M | yaml | weights |
| YOLOv5 | S | 32 * 8 | 640 | MS COCO 2017 | 37.6 | 787.34 | 7.2M | yaml | weights |
- Box mAP: Accuracy reported on the validation set.
- We refer to the official YOLOV5 to reproduce the P5 series model, and the differences are as follows:
- We use 8x NPU(Ascend910) for training, and the single-NPU batch size is 32. This is different from the official code.
Please refer to the GETTING_STARTED in MindYOLO for details.
It is easy to reproduce the reported results with the pre-defined training recipe. For distributed training on multiple Ascend 910 devices, please run
# distributed training on multiple GPU/Ascend devices
msrun --worker_num=8 --local_worker_num=8 --bind_core=True --log_dir=./yolov5_log python train.py --config ./configs/yolov5/yolov5n.yaml --device_target Ascend --is_parallel TrueSimilarly, you can train the model on multiple GPU devices with the above msrun command. Note: For more information about msrun configuration, please refer to here.
For detailed illustration of all hyper-parameters, please refer to config.py.
Note: As the global batch size (batch_size x num_devices) is an important hyper-parameter, it is recommended to keep the global batch size unchanged for reproduction or adjust the learning rate linearly to a new global batch size.
If you want to train or finetune the model on a smaller dataset without distributed training, please run:
# standalone training on a CPU/GPU/Ascend device
python train.py --config ./configs/yolov5/yolov5n.yaml --device_target AscendTo validate the accuracy of the trained model, you can use test.py and parse the checkpoint path with --weight.
python test.py --config ./configs/yolov5/yolov5n.yaml --device_target Ascend --weight /PATH/TO/WEIGHT.ckpt
See here.
[1] Jocher Glenn. YOLOv5 release v6.1. https://github.com/ultralytics/yolov5/releases/tag/v6.1, 2022.