yolov9 - 论文实现——YOLOv9: Learning What You Want to Learn using Programmable Gradient Information（利用可编程梯度信息学习您想学的内容）
Implementation of paper - YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information
MS COCO
| Model | Test Size | APval | AP50val | AP75val | Param. | FLOPs |
|---|---|---|---|---|---|---|
| YOLOv9-S | 640 | 46.8% | 63.4% | 50.7% | 7.1M | 26.4G |
| YOLOv9-M | 640 | 51.4% | 68.1% | 56.1% | 20.0M | 76.3G |
| YOLOv9-C | 640 | 53.0% | 70.2% | 57.8% | 25.3M | 102.1G |
| YOLOv9-E | 640 | 55.6% | 72.8% | 60.6% | 57.3M | 189.0G |
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Custom training: https://github.com/WongKinYiu/yolov9/issues/30#issuecomment-1960955297
ONNX export: https://github.com/WongKinYiu/yolov9/issues/2#issuecomment-1960519506 https://github.com/WongKinYiu/yolov9/issues/40#issue-2150697688
TensorRT inference: https://github.com/WongKinYiu/yolov9/issues/34#issue-2150393690 https://github.com/WongKinYiu/yolov9/issues/79#issue-2153547004
Hugging Face demo: https://github.com/WongKinYiu/yolov9/issues/45#issuecomment-1961496943
CoLab demo: https://github.com/WongKinYiu/yolov9/pull/18
ONNXSlim export: https://github.com/WongKinYiu/yolov9/pull/37
YOLOv9 ByteTrack: https://github.com/WongKinYiu/yolov9/issues/78#issue-2153512879
YOLOv9 counting: https://github.com/WongKinYiu/yolov9/issues/84#issue-2153904804
AnyLabeling tool: https://github.com/WongKinYiu/yolov9/issues/48#issue-2152139662
Docker environment (recommended)
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# create the docker container, you can change the share memory size if you have more.
nvidia-docker run --name yolov9 -it -v your_coco_path/:/coco/ -v your_code_path/:/yolov9 --shm-size=64g nvcr.io/nvidia/pytorch:21.11-py3
# apt install required packages
apt update
apt install -y zip htop screen libgl1-mesa-glx
# pip install required packages
pip install seaborn thop
# go to code folder
cd /yolov9yolov9-c-converted.pt
yolov9-e-converted.pt
yolov9-c.pt
yolov9-e.pt
gelan-c.pt
gelan-e.pt
# evaluate converted yolov9 models
python val.py --data data/coco.yaml --img 640 --batch 32 --conf 0.001 --iou 0.7 --device 0 --weights './yolov9-c-converted.pt' --save-json --name yolov9_c_c_640_val
# evaluate yolov9 models
#python val_dual.py --data data/coco.yaml --img 640 --batch 32 --conf 0.001 --iou 0.7 --device 0 --weights './yolov9-c.pt' --save-json --name yolov9_c_640_val
# evaluate gelan models
# python val.py --data data/coco.yaml --img 640 --batch 32 --conf 0.001 --iou 0.7 --device 0 --weights './gelan-c.pt' --save-json --name gelan_c_640_valYou will get the results:
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.530 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.702 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.578 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.362 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.585 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.693 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.392 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.652 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.702 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.541 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.760 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.844
Data preparation
bash scripts/get_coco.sh- Download MS COCO dataset images (train, val, test) and labels. If you have previously used a different version of YOLO, we strongly recommend that you delete and files, and redownload labels
train2017.cache
val2017.cache
Single GPU training
# train yolov9 models
python train_dual.py --workers 8 --device 0 --batch 16 --data data/coco.yaml --img 640 --cfg models/detect/yolov9-c.yaml --weights '' --name yolov9-c --hyp hyp.scratch-high.yaml --min-items 0 --epochs 500 --close-mosaic 15
# train gelan models
# python train.py --workers 8 --device 0 --batch 32 --data data/coco.yaml --img 640 --cfg models/detect/gelan-c.yaml --weights '' --name gelan-c --hyp hyp.scratch-high.yaml --min-items 0 --epochs 500 --close-mosaic 15Multiple GPU training
# train yolov9 models
python -m torch.distributed.launch --nproc_per_node 8 --master_port 9527 train_dual.py --workers 8 --device 0,1,2,3,4,5,6,7 --sync-bn --batch 128 --data data/coco.yaml --img 640 --cfg models/detect/yolov9-c.yaml --weights '' --name yolov9-c --hyp hyp.scratch-high.yaml --min-items 0 --epochs 500 --close-mosaic 15
# train gelan models
# python -m torch.distributed.launch --nproc_per_node 4 --master_port 9527 train.py --workers 8 --device 0,1,2,3 --sync-bn --batch 128 --data data/coco.yaml --img 640 --cfg models/detect/gelan-c.yaml --weights '' --name gelan-c --hyp hyp.scratch-high.yaml --min-items 0 --epochs 500 --close-mosaic 15@article{wang2024yolov9,
title={{YOLOv9}: Learning What You Want to Learn Using Programmable Gradient Information},
author={Wang, Chien-Yao and Liao, Hong-Yuan Mark},
booktitle={arXiv preprint arXiv:2402.13616},
year={2024}
}
@article{chang2023yolor,
title={{YOLOR}-Based Multi-Task Learning},
author={Chang, Hung-Shuo and Wang, Chien-Yao and Wang, Richard Robert and Chou, Gene and Liao, Hong-Yuan Mark},
journal={arXiv preprint arXiv:2309.16921},
year={2023}
}
Parts of code of YOLOR-Based Multi-Task Learning are released in the repository.
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