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-# YOLOv9
-Implementation of paper - [YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information](https://arxiv.org/abs/2402.13616)
-[![arxiv.org](http://img.shields.io/badge/cs.CV-arXiv%3A2402.13616-B31B1B.svg)](https://arxiv.org/abs/2402.13616)
-[![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/kadirnar/Yolov9)
-[![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/merve/yolov9)
-[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/train-yolov9-object-detection-on-custom-dataset.ipynb)
-[![OpenCV](https://img.shields.io/badge/OpenCV-BlogPost-black?logo=opencv&labelColor=blue&color=black)](https://learnopencv.com/yolov9-advancing-the-yolo-legacy/)
-<div align="center">
-    <a href="./">
-        <img src="./figure/performance.png" width="79%"/>
-    </a>
-</div>
-## Performance
-MS COCO
-| Model | Test Size | AP<sup>val</sup> | AP<sub>50</sub><sup>val</sup> | AP<sub>75</sub><sup>val</sup> | 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**](https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-c-converted.pt) | 640 | **53.0%** | **70.2%** | **57.8%** | **25.3M** | **102.1G** |
-| [**YOLOv9-E**](https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-e-converted.pt) | 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
-<details><summary> <b>Expand</b> </summary>
-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
-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 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
-</details>
-## Installation
-Docker environment (recommended)
-<details><summary> <b>Expand</b> </summary>
-``` shell
-# 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
-```
-</details>
-## Evaluation
-[`yolov9-c-converted.pt`](https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-c-converted.pt) [`yolov9-e-converted.pt`](https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-e-converted.pt) [`yolov9-c.pt`](https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-c.pt) [`yolov9-e.pt`](https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-e.pt) [`gelan-c.pt`](https://github.com/WongKinYiu/yolov9/releases/download/v0.1/gelan-c.pt) [`gelan-e.pt`](https://github.com/WongKinYiu/yolov9/releases/download/v0.1/gelan-e.pt)
-``` shell
-# 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
-``` shell
-bash scripts/get_coco.sh
-```
-* Download MS COCO dataset images ([train](http://images.cocodataset.org/zips/train2017.zip), [val](http://images.cocodataset.org/zips/val2017.zip), [test](http://images.cocodataset.org/zips/test2017.zip)) and [labels](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/coco2017labels-segments.zip). 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](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/coco2017labels-segments.zip)
-Single GPU training
-``` shell
-# 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
-``` shell
-# 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
-```
-## Re-parameterization
-See [reparameterization.ipynb](https://github.com/WongKinYiu/yolov9/blob/main/tools/reparameterization.ipynb).
-## Inference
-<div align="center">
-    <a href="./">
-        <img src="./figure/horses_prediction.jpg" width="49%"/>
-    </a>
-</div>
-``` shell
-# inference converted yolov9 models
-python detect.py --source './data/images/horses.jpg' --img 640 --device 0 --weights './yolov9-c-converted.pt' --name yolov9_c_c_640_detect
-# inference yolov9 models
-# python detect_dual.py --source './data/images/horses.jpg' --img 640 --device 0 --weights './yolov9-c.pt' --name yolov9_c_640_detect
-# inference gelan models
-# python detect.py --source './data/images/horses.jpg' --img 640 --device 0 --weights './gelan-c.pt' --name gelan_c_c_640_detect
-```
-## Citation
-```
-@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}
-}
-```
-## Teaser
-Parts of code of [YOLOR-Based Multi-Task Learning](https://arxiv.org/abs/2309.16921) are released in the repository.
-<div align="center">
-    <a href="./">
-        <img src="./figure/multitask.png" width="99%"/>
-    </a>
-</div>
-#### Object Detection
-[`gelan-c-det.pt`](https://github.com/WongKinYiu/yolov9/releases/download/v0.1/gelan-c-det.pt)
-`object detection`
-``` shell
-# coco/labels/{split}/*.txt
-# bbox or polygon (1 instance 1 line)
-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-det --hyp hyp.scratch-high.yaml --min-items 0 --epochs 300 --close-mosaic 10
-```
-| Model | Test Size | Param. | FLOPs | AP<sup>box</sup> |
-| :-- | :-: | :-: | :-: | :-: |
-| [**GELAN-C-DET**](https://github.com/WongKinYiu/yolov9/releases/download/v0.1/gelan-c-det.pt) | 640 | 25.3M | 102.1G |**52.3%** |
-| [**YOLOv9-C-DET**]() | 640 | 25.3M | 102.1G | **53.0%** |
-#### Instance Segmentation
-[`gelan-c-seg.pt`](https://github.com/WongKinYiu/yolov9/releases/download/v0.1/gelan-c-seg.pt)
-`object detection` `instance segmentation`
-``` shell
-# coco/labels/{split}/*.txt
-# polygon (1 instance 1 line)
-python segment/train.py --workers 8 --device 0 --batch 32  --data coco.yaml --img 640 --cfg models/segment/gelan-c-seg.yaml --weights '' --name gelan-c-seg --hyp hyp.scratch-high.yaml --no-overlap --epochs 300 --close-mosaic 10
-```
-| Model | Test Size | Param. | FLOPs | AP<sup>box</sup> | AP<sup>mask</sup>  |
-| :-- | :-: | :-: | :-: | :-: | :-: |
-| [**GELAN-C-SEG**](https://github.com/WongKinYiu/yolov9/releases/download/v0.1/gelan-c-seg.pt) | 640 | 27.4M | 144.6G | **52.3%** | **42.4%** |
-| [**YOLOv9-C-SEG**]() | 640 | 27.4M | 145.5G | **53.3%** | **43.5%** |
-#### Panoptic Segmentation
-[`gelan-c-pan.pt`](https://github.com/WongKinYiu/yolov9/releases/download/v0.1/gelan-c-pan.pt)
-`object detection` `instance segmentation` `semantic segmentation` `stuff segmentation` `panoptic segmentation`
-``` shell
-# coco/labels/{split}/*.txt
-# polygon (1 instance 1 line)
-# coco/stuff/{split}/*.txt
-# polygon (1 semantic 1 line)
-python panoptic/train.py --workers 8 --device 0 --batch 32  --data coco.yaml --img 640 --cfg models/panoptic/gelan-c-pan.yaml --weights '' --name gelan-c-pan --hyp hyp.scratch-high.yaml --no-overlap --epochs 300 --close-mosaic 10
-```
-| Model | Test Size | Param. | FLOPs | AP<sup>box</sup> | AP<sup>mask</sup>  | mIoU<sub>164k/10k</sub><sup>semantic</sup> | mIoU<sup>stuff</sup> | PQ<sup>panoptic</sup> |
-| :-- | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: |
-| [**GELAN-C-PAN**](https://github.com/WongKinYiu/yolov9/releases/download/v0.1/gelan-c-pan.pt) | 640 | 27.6M | 146.7G | **52.6%** | **42.5%** | **39.0%/48.3%** | **52.7%** | **39.4%** |
-| [**YOLOv9-C-PAN**]() | 640 | 28.8M | 187.0G | **52.7%** | **43.0%** | **39.8%/-** | **52.2%** | **40.5%** |
-#### Image Captioning (not yet released)
-<!--[`gelan-c-cap.pt`]()-->
-`object detection` `instance segmentation` `semantic segmentation` `stuff segmentation` `panoptic segmentation` `image captioning`
-``` shell
-# coco/labels/{split}/*.txt
-# polygon (1 instance 1 line)
-# coco/stuff/{split}/*.txt
-# polygon (1 semantic 1 line)
-# coco/annotations/*.json
-# json (1 split 1 file)
-python caption/train.py --workers 8 --device 0 --batch 32  --data coco.yaml --img 640 --cfg models/caption/gelan-c-cap.yaml --weights '' --name gelan-c-cap --hyp hyp.scratch-high.yaml --no-overlap --epochs 300 --close-mosaic 10
-```
-| Model | Test Size | Param. | FLOPs |  AP<sup>box</sup> | AP<sup>mask</sup>  | mIoU<sub>164k/10k</sub><sup>semantic</sup>  | mIoU<sup>stuff</sup> | PQ<sup>panoptic</sup> | BLEU@4<sup>caption</sup> | CIDEr<sup>caption</sup> |
-| :-- | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: |
-| [**GELAN-C-CAP**]() | 640 | 47.5M | - | **51.9%** | **42.6%** | **42.5%/-** | **56.5%** | **41.7%** | **38.8** | **122.3** |
-| [**YOLOv9-C-CAP**]() | 640 | 47.5M | - | **52.1%** | **42.6%** | **43.0%/-** | **56.4%** | **42.1%** | **39.1** | **122.0** |
-<!--| [**YOLOR-MT**]() | 640 | 79.3M | - | **51.0%** | **41.7%** | **-/49.6%** | **55.9%** | **40.5%** | **35.7** | **112.7** |-->
-## Acknowledgements
-<details><summary> <b>Expand</b> </summary>
-* [https://github.com/AlexeyAB/darknet](https://github.com/AlexeyAB/darknet)
-* [https://github.com/WongKinYiu/yolor](https://github.com/WongKinYiu/yolor)
-* [https://github.com/WongKinYiu/yolov7](https://github.com/WongKinYiu/yolov7)
-* [https://github.com/VDIGPKU/DynamicDet](https://github.com/VDIGPKU/DynamicDet)
-* [https://github.com/DingXiaoH/RepVGG](https://github.com/DingXiaoH/RepVGG)
-* [https://github.com/ultralytics/yolov5](https://github.com/ultralytics/yolov5)
-* [https://github.com/meituan/YOLOv6](https://github.com/meituan/YOLOv6)
-</details>