Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
Shamelessly Copied and integrated by Ben @ Hivemapper
yolov7.pt
yolov7x.pt
yolov7-w6.pt
yolov7-e6.pt
yolov7-d6.pt
yolov7-e6e.pt
python test.py --data data/coco.yaml --img 640 --batch 32 --conf 0.001 --iou 0.65 --device 0 --weights yolov7.pt --name yolov7_640_val
Data preparation
- Build a Manifest (use
../coco_json_to_hivemapper_annotation.py
) - Upload that manifest to
s3://network-machine-learning-data/${NEW_MANIFEST}.jsonl
- Invoke Training using the
--bucket network-machine-learning-data --key ${NEW_MANIFEST}.jsonl
args todriver.py
Single GPU training
# train p5 models
python driver.py \
--bucket network-machine-learning-data
--key path/to/manifest.jsonl
--image-bucket network-processed-data
--workers 8
--device 0
--batch-size 32
--data data/coco.yaml
--img 640 640
--cfg cfg/training/yolov7.yaml
--weights ''
--name yolov7
--hyp data/hyp.scratch.p5.yaml
yolov7_training.pt
yolov7x_training.pt
yolov7-w6_training.pt
yolov7-e6_training.pt
yolov7-d6_training.pt
yolov7-e6e_training.pt
Single GPU finetuning for custom dataset
# finetune p5 models
python train.py --workers 8 --device 0 --batch-size 32 --data data/custom.yaml --img 640 640 --cfg cfg/training/yolov7-custom.yaml --weights 'yolov7_training.pt' --name yolov7-custom --hyp data/hyp.scratch.custom.yaml
# finetune p6 models
python train_aux.py --workers 8 --device 0 --batch-size 16 --data data/custom.yaml --img 1280 1280 --cfg cfg/training/yolov7-w6-custom.yaml --weights 'yolov7-w6_training.pt' --name yolov7-w6-custom --hyp data/hyp.scratch.custom.yaml
On video:
python detect.py --weights yolov7.pt --conf 0.25 --img-size 640 --source yourvideo.mp4
On image:
python detect.py --weights yolov7.pt --conf 0.25 --img-size 640 --source inference/images/horses.jpg
This modified distribution of YoloV7 automatically exports pytorch weights to MLFlow, and stores the model weights in s3 for archival.
Pytorch to CoreML (and inference on MacOS/iOS)
Pytorch to ONNX with NMS (and inference)
python export.py --weights yolov7-tiny.pt --grid --end2end --simplify \
--topk-all 100 --iou-thres 0.65 --conf-thres 0.35 --img-size 640 640 --max-wh 640
Pytorch to TensorRT with NMS (and inference)
wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-tiny.pt
python export.py --weights ./yolov7-tiny.pt --grid --end2end --simplify --topk-all 100 --iou-thres 0.65 --conf-thres 0.35 --img-size 640 640
git clone https://github.com/Linaom1214/tensorrt-python.git
python ./tensorrt-python/export.py -o yolov7-tiny.onnx -e yolov7-tiny-nms.trt -p fp16
Pytorch to TensorRT another way
Expand
wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-tiny.pt
python export.py --weights yolov7-tiny.pt --grid --include-nms
git clone https://github.com/Linaom1214/tensorrt-python.git
python ./tensorrt-python/export.py -o yolov7-tiny.onnx -e yolov7-tiny-nms.trt -p fp16
# Or use trtexec to convert ONNX to TensorRT engine
/usr/src/tensorrt/bin/trtexec --onnx=yolov7-tiny.onnx --saveEngine=yolov7-tiny-nms.trt --fp16
Tested with: Python 3.7.13, Pytorch 1.12.0+cu113
See keypoint.ipynb.
See instance.ipynb.
@article{wang2022yolov7,
title={{YOLOv7}: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors},
author={Wang, Chien-Yao and Bochkovskiy, Alexey and Liao, Hong-Yuan Mark},
journal={arXiv preprint arXiv:2207.02696},
year={2022}
}
Yolov7-semantic & YOLOv7-panoptic
Expand
- https://github.com/AlexeyAB/darknet
- https://github.com/WongKinYiu/yolor
- https://github.com/WongKinYiu/PyTorch_YOLOv4
- https://github.com/WongKinYiu/ScaledYOLOv4
- https://github.com/Megvii-BaseDetection/YOLOX
- https://github.com/ultralytics/yolov3
- https://github.com/ultralytics/yolov5
- https://github.com/DingXiaoH/RepVGG
- https://github.com/JUGGHM/OREPA_CVPR2022
- https://github.com/TexasInstruments/edgeai-yolov5/tree/yolo-pose