# yolo图像识别 **Repository Path**: SailorCoder/yolo-tutorial ## Basic Information - **Project Name**: yolo图像识别 - **Description**: No description available - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 2 - **Created**: 2026-07-14 - **Last Updated**: 2026-07-14 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # YOLO_Tutorial English | [简体中文](https://github.com/yjh0410/YOLO_Tutorial/blob/main/README_CN.md) # Introduction Here is the source code for an introduction to YOLO. We adopted the core concepts of **YOLOv1~v4**, **YOLOX** and **YOLOv7** for this project and made the necessary adjustments. By learning how to construct the well-known YOLO detector, we hope that newcomers can enter the field of object detection without any difficulty. **Book**: The technical books that go along with this project's code is being reviewed, please be patient. ## Requirements - We recommend you to use Anaconda to create a conda environment: ```Shell conda create -n yolo python=3.6 ``` - Then, activate the environment: ```Shell conda activate yolo ``` - Requirements: ```Shell pip install -r requirements.txt ``` My environment: - PyTorch = 1.9.1 - Torchvision = 0.10.1 At least, please make sure your torch is version 1.x. ## Experiments ### VOC - Download VOC. ```Shell cd cd dataset/scripts/ sh VOC2007.sh sh VOC2012.sh ``` - Check VOC ```Shell cd python dataset/voc.py ``` - Train on VOC For example: ```Shell python train.py --cuda -d voc --root path/to/VOCdevkit -m yolov1 -bs 16 --max_epoch 150 --wp_epoch 1 --eval_epoch 10 --fp16 --ema --multi_scale ``` | Model | Backbone | Scale | IP | Epoch | APval
0.5 | Weight | |--------------|---------------------|-------|------|-------|-------------------|--------| | YOLOv1 | ResNet-18 | 640 | √ | 150 | 76.7 | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov1_voc.pth) | | YOLOv2 | DarkNet-19 | 640 | √ | 150 | 79.8 | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov2_voc.pth) | | YOLOv3 | DarkNet-53 | 640 | √ | 150 | 82.0 | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov3_voc.pth) | | YOLOv4 | CSPDarkNet-53 | 640 | √ | 150 | 83.6 | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov4_voc.pth) | | YOLOX-L | CSPDarkNet-L | 640 | √ | 150 | 84.6 | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolox_voc.pth) | | YOLOv7-Large | ELANNet-Large | 640 | √ | 150 | 86.0 | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov7_large_voc.pth) | *All models are trained with ImageNet pretrained weight (IP). All FLOPs are measured with a 640x640 image size on VOC2007 test. The FPS is measured with batch size 1 on 3090 GPU from the model inference to the NMS operation.* ### COCO - Download COCO. ```Shell cd cd dataset/scripts/ sh COCO2017.sh ``` - Check COCO ```Shell cd python dataset/coco.py ``` - Train on COCO For example: ```Shell python train.py --cuda -d coco --root path/to/COCO -m yolov1 -bs 16 --max_epoch 150 --wp_epoch 1 --eval_epoch 10 --fp16 --ema --multi_scale ``` ## Train ### Single GPU ```Shell sh train_single_gpu.sh ``` You can change the configurations of `train_single_gpu.sh`, according to your own situation. You also can add `--vis_tgt` to check the images and targets during the training stage. For example: ```Shell python train.py --cuda -d coco --root path/to/coco -m yolov1 --vis_tgt ``` ### Multi GPUs ```Shell sh train_multi_gpus.sh ``` You can change the configurations of `train_multi_gpus.sh`, according to your own situation. **In the event of a training interruption**, you can pass `--resume` the latest training weight path (`None` by default) to resume training. For example: ```Shell python train.py \ --cuda \ -d coco \ -m yolov1 \ -bs 16 \ --max_epoch 300 \ --wp_epoch 3 \ --eval_epoch 10 \ --ema \ --fp16 \ --resume weights/coco/yolov1/yolov1_epoch_151_39.24.pth ``` Then, training will continue from 151 epoch. ## Test ```Shell python test.py -d coco \ --cuda \ -m yolov1 \ --img_size 640 \ --weight path/to/weight \ --root path/to/dataset/ \ --show ``` For YOLOv7, since it uses the RepConv in PaFPN, you can add `--fuse_repconv` to fuse the RepConv block. ```Shell python test.py -d coco \ --cuda \ -m yolov7_large \ --fuse_repconv \ --img_size 640 \ --weight path/to/weight \ --root path/to/dataset/ \ --show ``` ## Evaluation ```Shell python eval.py -d coco-val \ --cuda \ -m yolov1 \ --img_size 640 \ --weight path/to/weight \ --root path/to/dataset/ \ --show ``` ## Demo I have provide some images in `data/demo/images/`, so you can run following command to run a demo: ```Shell python demo.py --mode image \ --path_to_img data/demo/images/ \ --cuda \ --img_size 640 \ -m yolov2 \ --weight path/to/weight \ --show ``` If you want run a demo of streaming video detection, you need to set `--mode` to `video`, and give the path to video `--path_to_vid`。 ```Shell python demo.py --mode video \ --path_to_vid data/demo/videos/your_video \ --cuda \ --img_size 640 \ -m yolov2 \ --weight path/to/weight \ --show \ --gif ``` If you want run video detection with your camera, you need to set `--mode` to `camera`。 ```Shell python demo.py --mode camera \ --cuda \ --img_size 640 \ -m yolov2 \ --weight path/to/weight \ --show \ --gif ``` ### Detection visualization * Detector: YOLOv2 Command： ```Shell python demo.py --mode video \ --path_to_vid ./dataset/demo/videos/000006.mp4 \ --cuda \ --img_size 640 \ -m yolov2 \ --weight path/to/weight \ --show \ --gif ``` Results: ![image](./img_files/video_detection_demo.gif) ## Train on custom dataset Besides the popular datasets, we can also train the model on ourself dataset. To achieve this goal, you should follow these steps: - Step-1: Prepare the images (JPG/JPEG/PNG ...) and use `labelimg` to make XML format annotation files. ``` OurDataset |_ train | |_ images | |_ 0.jpg | |_ 1.jpg | |_ ... | |_ annotations | |_ 0.xml | |_ 1.xml | |_ ... |_ val | |_ images | |_ 0.jpg | |_ 1.jpg | |_ ... | |_ annotations | |_ 0.xml | |_ 1.xml | |_ ... | ... ``` - Step-2: Convert ourdataset to COCO format. ```Shell cd cd tools # convert train split python convert_ours_to_coco.py --root path/to/dataset/ --split train # convert val split python convert_ours_to_coco.py --root path/to/dataset/ --split val ``` Then, we can get a `train.json` file and a `val.json` file, as shown below. ``` OurDataset |_ train | |_ images | |_ 0.jpg | |_ 1.jpg | |_ ... | |_ annotations | |_ 0.xml | |_ 1.xml | |_ ... | |_ train.json |_ val | |_ images | |_ 0.jpg | |_ 1.jpg | |_ ... | |_ annotations | |_ 0.xml | |_ 1.xml | |_ ... | |_ val.json | ... ``` - Step-3 Define our class labels. Please open `dataset/ourdataset.py` file and change `our_class_labels = ('cat',)` according to our definition of categories. - Step-4 Check ```Shell cd cd dataset # convert train split python ourdataset.py --root path/to/dataset/ --split train # convert val split python ourdataset.py --root path/to/dataset/ --split val ``` - Step-5 **Train** For example: ```Shell cd python train.py --root path/to/dataset/ -d ourdataset -m yolov1 -bs 16 --max_epoch 100 --wp_epoch 1 --eval_epoch 5 -p path/to/yolov1_coco.pth ``` - Step-6 **Test** For example: ```Shell cd python test.py --root path/to/dataset/ -d ourdataset -m yolov1 --weight path/to/checkpoint --show ``` - Step-7 **Eval** For example: ```Shell cd python eval.py --root path/to/dataset/ -d ourdataset -m yolov1 --weight path/to/checkpoint ``` ## Deployment 1. [ONNX export and an ONNXRuntime](./deployment/ONNXRuntime/)