See our YOLOv5 PyTorch Hub Tutorial for details.Wow, this script is really cool, especially for installing multiple libraries! But a quick reminder that you can also install any API from the command line of your terminal app. YOLOv5 accepts URL, Filename, PIL, OpenCV, Numpy and PyTorch inputs, and returns detections in torch, pandas, and JSON output formats. This example loads a pretrained YOLOv5s model and passes an image for inference. * **Reproduce** by `python test.py -task study -data coco.yaml -iou 0.7 -weights yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5圆.pt` * EfficientDet data from () at batch size 8. * GPU Speed measures end-to-end time per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size 32, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS. **Reproduce TTA** by `python test.py -data coco.yaml -img 1536 -iou 0.7 -augment` * Test Time Augmentation (()) includes reflection and scale augmentation. * All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation). **Reproduce speed** by `python test.py -data coco.yaml -img 640 -conf 0.25 -iou 0.45` * Speed GPU averaged over 5000 COCO val2017 images using a GCP () V100 instance, and includes FP16 inference, postprocessing and NMS. **Reproduce mAP** by `python test.py -data coco.yaml -img 640 -conf 0.001 -iou 0.65` * AP values are for single-model single-scale unless otherwise noted. * AP test denotes COCO () server results, all other AP results denote val2017 accuracy. YOLOv5 🚀 is a family of compound-scaled object detection models trained on the COCO dataset, and includes simple functionality for Test Time Augmentation (TTA), model ensembling, hyperparameter evolution, and export to ONNX, CoreML and TFLite.
Pip install -qr # install dependencies Model Description
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