YOLOv9

Implementation of paper - YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information

<div align="center"> <a href="./"> <img src="./figure/performance.png" width="79%"/> </a> </div>

Performance

MS COCO

| Model | Test Size | AP^val | AP₅₀^val | AP₇₅^val | Param. | FLOPs | | :-- | :-: | :-: | :-: | :-: | :-: | :-: | | YOLOv9-T | 640 | 38.3% | 53.1% | 41.3% | 2.0M | 7.7G | | 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 |

<!-- | [**YOLOv9 (ReLU)**]() | 640 | **51.9%** | **69.1%** | **56.5%** | **25.3M** | **102.1G** | --> <!-- tiny, small, and medium models will be released after the paper be accepted and published. -->

Useful Links

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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 https://github.com/WongKinYiu/yolov9/issues/130#issue-2162045461

ONNX export for segmentation: https://github.com/WongKinYiu/yolov9/issues/260#issue-2191162150

TensorRT inference: https://github.com/WongKinYiu/yolov9/issues/143#issuecomment-1975049660 https://github.com/WongKinYiu/yolov9/issues/34#issue-2150393690 https://github.com/WongKinYiu/yolov9/issues/79#issue-2153547004 https://github.com/WongKinYiu/yolov9/issues/143#issue-2164002309

QAT TensorRT: https://github.com/WongKinYiu/yolov9/issues/327#issue-2229284136 https://github.com/WongKinYiu/yolov9/issues/253#issue-2189520073

TensorRT inference for segmentation: https://github.com/WongKinYiu/yolov9/issues/446

TFLite: https://github.com/WongKinYiu/yolov9/issues/374#issuecomment-2065751706

OpenVINO: https://github.com/WongKinYiu/yolov9/issues/164#issue-2168540003

C# ONNX inference: https://github.com/WongKinYiu/yolov9/issues/95#issue-2155974619

C# OpenVINO inference: https://github.com/WongKinYiu/yolov9/issues/95#issuecomment-1968131244

OpenCV: https://github.com/WongKinYiu/yolov9/issues/113#issuecomment-1971327672

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 ROS: https://github.com/WongKinYiu/yolov9/issues/144#issue-2164210644

YOLOv9 ROS TensorRT: https://github.com/WongKinYiu/yolov9/issues/145#issue-2164218595

YOLOv9 Julia: https://github.com/WongKinYiu/yolov9/issues/141#issuecomment-1973710107

YOLOv9 MLX: https://github.com/WongKinYiu/yolov9/issues/258#issue-2190586540

YOLOv9 StrongSORT with OSNet: https://github.com/WongKinYiu/yolov9/issues/299#issue-2212093340

YOLOv9 ByteTrack: https://github.com/WongKinYiu/yolov9/issues/78#issue-2153512879

YOLOv9 DeepSORT: https://github.com/WongKinYiu/yolov9/issues/98#issue-2156172319

YOLOv9 counting: https://github.com/WongKinYiu/yolov9/issues/84#issue-2153904804

YOLOv9 speed estimation: https://github.com/WongKinYiu/yolov9/issues/456

YOLOv9 face detection: https://github.com/WongKinYiu/yolov9/issues/121#issue-2160218766

YOLOv9 segmentation onnxruntime: https://github.com/WongKinYiu/yolov9/issues/151#issue-2165667350

Comet logging: https://github.com/WongKinYiu/yolov9/pull/110

MLflow logging: https://github.com/WongKinYiu/yolov9/pull/87

AnyLabeling tool: https://github.com/WongKinYiu/yolov9/issues/48#issue-2152139662

AX650N deploy: https://github.com/WongKinYiu/yolov9/issues/96#issue-2156115760

Conda environment: https://github.com/WongKinYiu/yolov9/pull/93

AutoDL docker environment: https://github.com/WongKinYiu/yolov9/issues/112#issue-2158203480

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Installation

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 /yolov9

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Evaluation

yolov9-s-converted.pt yolov9-m-converted.pt yolov9-c-converted.pt yolov9-e-converted.pt yolov9-s.pt yolov9-m.pt yolov9-c.pt yolov9-e.pt gelan-s.pt gelan-m.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_val

You 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

Training

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 train2017.cache and val2017.cache files, and redownload labels

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 15

Multiple 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-