YOLOv9玉米叶病害检测权重，包含1500玉米叶病害检测数据集

# YOLOv9 Implementation of paper - [YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information](https://arxiv.org/abs/2402.13616) [](https://arxiv.org/abs/2402.13616) [](https://huggingface.co/spaces/kadirnar/Yolov9) [](https://huggingface.co/merve/yolov9) [](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/train-yolov9-object-detection-on-custom-dataset.ipynb) [](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^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**](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