# 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 strong
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YOLOv9玉米叶病害检测权重,包含1500玉米叶病害检测数据集
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YOLOv9玉米叶病害检测权重,包含1500玉米叶病害检测数据集 ;数据集目录已经配置好,划分好 train,val, test,并附有data.yaml文件,yolov5、yolov7、yolov8,yolov9等算法可以直接进行训练模型,txt格式标签, 数据集和检测结果参考:https://blog.csdn.net/zhiqingAI/article/details/124230743 数据集配置目录结构data.yaml: train: E:\python_code\dataset\Corn_yumiye_bad_dataset\train/images val: E:\python_code\dataset\Corn_yumiye_bad_dataset\valid/images test: E:\python_code\dataset\Corn_yumiye_bad_dataset\test/images nc: 4 names: - blight - common_rust - gray_leaf_spot - healthy
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