Companion repo for the blogpost.
If you like what you see, support me in keeping the lights on to produce more posts like this.
git clone https://github.com/dnth/yolov5-deepsparse-blogpost
cd yolov5-deepsparse-blogpost/
pip install torch==1.9.0 torchvision==0.10.0 --extra-index-url https://download.pytorch.org/whl/cu111
pip install -r req.txt
Or (Highly recommended!) 👇
The easiest way to get started is to run this Colab Notebook.
The notebook serves as a guide to:
- Install all packages used this blog post.
- Train a sparse YOLOv5 models using
SparseML. - Run inference using the
DeepSparseengine.
python train.py --cfg ./models_v5.0/yolov5s.yaml --data pistols.yaml --hyp data/hyps/hyp.scratch.yaml --weights yolov5s.pt --img 416 --batch-size 64 --optimizer SGD --epochs 100 --device 0 --project yolov5-deepsparse --name yolov5s-sgd
python train.py --cfg ./models_v5.0/yolov5s.yaml --recipe ../recipes/yolov5s.pruned.md --data pistols.yaml --hyp data/hyps/hyp.scratch.yaml --weights yolov5-deepsparse/yolov5s-sgd/weights/best.pt --img 416 --batch-size 64 --optimizer SGD --epochs 100 --device 0 --project yolov5-deepsparse --name yolov5s-sgd-one-shot --one-shot
python train.py --cfg ./models_v5.0/yolov5s.yaml --recipe ../recipes/yolov5s.pruned.md --data pistols.yaml --hyp data/hyps/hyp.scratch.yaml --weights yolov5s.pt --img 416 --batch-size 64 --optimizer SGD --device 0 --project yolov5-deepsparse --name yolov5s-sgd-pruned
python train.py --cfg ./models_v5.0/yolov5s.yaml --recipe ../recipes/yolov5s.quantized.md --data pistols.yaml --hyp data/hyps/hyp.scratch.yaml --weights yolov5-deepsparse/yolov5s-sgd/weights/best.pt --img 416 --batch-size 64 --project yolov5-deepsparse --name yolov5s-sgd-quantized
python train.py --cfg ./models_v5.0/yolov5s.yaml --recipe ../recipes/yolov5.transfer_learn_pruned_quantized.md --data pistols.yaml --hyp data/hyps/hyp.scratch.yaml --weights yolov5s.pt --img 416 --batch-size 64 --optimizer SGD --device 0 --project yolov5-deepsparse --name yolov5s-sgd-pruned-quantized
python train.py --data pistols.yaml --cfg ./models_v5.0/yolov5s.yaml --weights zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/pruned_quant-aggressive_94?recipe_type=transfer --img 416 --batch-size 64 --hyp data/hyps/hyp.scratch.yaml --recipe ../recipes/yolov5.transfer_learn_pruned_quantized.md --optimizer SGD --device 0 --project yolov5-deepsparse --name yolov5s-sgd-pruned-quantized-transfer
python train.py --cfg ./models_v5.0/yolov5n.yaml --recipe ../recipes/yolov5.transfer_learn_pruned_quantized.md --data pistols.yaml --hyp data/hyps/hyp.scratch.yaml --weights yolov5n.pt --img 416 --batch-size 64 --optimizer SGD --device 0 --project yolov5-deepsparse --name yolov5n-sgd-pruned-quantized
python export.py --weights yolov5-deepsparse/yolov5s-sgd/weights/best.pt --include onnx --imgsz 416 --dynamic --simplify
python export.py --weights yolov5-deepsparse/yolov5s-sgd-one-shot/weights/checkpoint-one-shot.pt --include onnx --imgsz 416 --dynamic --simplify
python export.py --weights yolov5-deepsparse/yolov5s-sgd-pruned/weights/best.pt --include onnx --imgsz 416 --dynamic --simplify
python export.py --weights yolov5-deepsparse/yolov5s-sgd-quantized/weights/best.pt --include onnx --imgsz 416 --dynamic --simplify
python export.py --weights yolov5-deepsparse/yolov5s-sgd-pruned-quantized/weights/best.pt --include onnx --imgsz 416 --dynamic --simplify
python export.py --weights yolov5-deepsparse/yolov5s-sgd-pruned-quantized-transfer/weights/best.pt --include onnx --imgsz 416 --dynamic --simplify
python export.py --weights yolov5-deepsparse/yolov5n-sgd-pruned-quantized/weights/best.pt --include onnx --imgsz 416 --dynamic --simplify
python annotate.py yolov5-deepsparse/yolov5s-sgd/weights/best.pt --source data/pexels-cottonbro-8717592.mp4 --engine torch --image-shape 416 416 --device cpu --conf-thres 0.7
python annotate.py yolov5-deepsparse/yolov5s-sgd/weights/best.onnx --source data/pexels-cottonbro-8717592.mp4 --engine deepsparse --device cpu --conf-thres 0.7 --image-shape 416 416 --num-cores 4
python annotate.py yolov5-deepsparse/yolov5s-sgd-one-shot/weights/checkpoint-one-shot.onnx --source data/pexels-cottonbro-8717592.mp4 --engine deepsparse --device cpu --conf-thres 0.7 --image-shape 416 416 --num-cores 4
python annotate.py yolov5-deepsparse/yolov5s-sgd-pruned/weights/best.onnx --source data/pexels-cottonbro-8717592.mp4 --engine deepsparse --device cpu --conf-thres 0.7 --image-shape 416 416 --num-cores 4
python annotate.py yolov5-deepsparse/yolov5s-sgd-quantized/weights/best.onnx --source data/pexels-cottonbro-8717592.mp4 --engine deepsparse --device cpu --conf-thres 0.7 --image-shape 416 416 --quantized-input --num-cores 4
python annotate.py yolov5-deepsparse/yolov5s-sgd-pruned-quantized/weights/best.onnx --source data/pexels-cottonbro-8717592.mp4 --engine deepsparse --device cpu --conf-thres 0.7 --image-shape 416 416 --quantized-input --num-cores 4
python annotate.py yolov5-deepsparse/yolov5s-sgd-pruned-quantized-transfer/weights/best.onnx --source data/pexels-cottonbro-8717592.mp4 --engine deepsparse --device cpu --conf-thres 0.8 --image-shape 416 416 --quantized-input --num-cores 4
python annotate.py yolov5-deepsparse/yolov5n-sgd-pruned-quantized-hardswish/weights/best.onnx --source data/pexels-cottonbro-8717592.mp4 --engine deepsparse --device cpu --conf-thres 0.7 --image-shape 416 416 --quantized-input --num-cores 4
https://wandb.ai/dnth/yolov5-deepsparse?workspace=user-dnth
python detect.py --weights yolov5-deepsparse/yolov5s-sgd/weights/best.pt --source data/pexels-cottonbro-8717592.mp4 --data data/pistols.yaml --imgsz 416 --view-img --nosave --device cpu