KO
|
EN
gitlite — search
Search
#javascript
#python
#hacktoberfest
#react
#ai
#typescript
#llm
#go
#golang
#android
#machine-learning
#rust
#deep-learning
#linux
start-scaled-yolov4
★ 10
Open GitHub ↗
Start Scaled YOLOv4
Download README (.md)
Explore Similar Repositories
pkupcl
:
北京大学物理化学实验报告写作的参考模板。
manitto-android
:
우리 모임 아이스브레이킹의 시작, 산타마니또
LivePushCameraMedia
:
项目实现了音视频编解码和直播功能。包括OpenGL的渲染，添加水印，类似抖音相机画面和音乐合成mp4，yuv数据解析，RTMP推流等
BlazQ
:
BlazQ is a series of live coding events helping beginners get a free, comprehensive, and fun way into software development. It's an excellent way to learn coding with C# and .NET and discover if you are serious about being a developer.
CNN-LSTM-attention
:
CNN-LSTM-attention
// repository documentation
Was this content helpful?
★ 0
(0 ratings)
Select Rating:
★
★
★
★
★
Submit Feedback
Recent Feedback
×
Download README
Do you want to download the
README.md
file for
start-scaled-yolov4
?
Download (.md)
# Start Scaled YOLOv4 ## Scaled-YOLOv4 - 代码: https://github.com/WongKinYiu/ScaledYOLOv4 - 论文: https://arxiv.org/abs/2011.08036 - 文章: https://alexeyab84.medium.com/scaled-yolo-v4-is-the-best-neural-network-for-object-detection-on-ms-coco-dataset-39dfa22fa982 ## 环境准备 ### 基础环境 - Nvidia 显卡的主机 - Ubuntu 18.04 - 系统安装，可见 [制作 USB 启动盘，及系统安装](https://mp.weixin.qq.com/s/HHXh0GKaxfaUdemcL2IdRQ) - Nvidia Driver - 驱动安装，可见 [Ubuntu 初始配置 - Nvidia 驱动](https://mp.weixin.qq.com/s/czgD2CQOnOZ_eslrRkq6LA) ### 开发环境 [Anaconda]: https://www.anaconda.com/products/individual#Downloads 下载并安装 [Anaconda][] ,之后于 Terminal 执行： ```bash # 创建 Python 虚拟环境 conda create -n scaled-yolov4 python=3.8 -y conda activate scaled-yolov4 # 安装 PyTorch with CUDA conda install pytorch==1.7.1 torchvision==0.8.2 cudatoolkit=10.2 -c pytorch -y ``` 注意： - `pytorch` 等版本请对照表 [torch, torchvision, python](https://github.com/pytorch/vision#installation) - `cudatoolkit` 版本请对照表 [CUDA Toolkit and Compatible Driver Versions](https://docs.nvidia.com/deploy/cuda-compatibility/index.html#binary-compatibility__table-toolkit-driver) - `conda` 下载过慢，可尝试配置国内镜像源 [CUDA Toolkit]: https://developer.nvidia.com/cuda-toolkit-archive 下载 [CUDA Toolkit][] ，其版本也注意对应 Nvidia 驱动版本。下一步需要。命令参考： ```bash wget https://developer.download.nvidia.com/compute/cuda/10.2/Prod/local_installers/cuda_10.2.89_440.33.01_linux.run sudo sh cuda_10.2.89_440.33.01_linux.run ``` 注意：安装时，请手动取消驱动安装选项。 [mish-cuda]: https://github.com/JunnYu/mish-cuda 下载 [mish-cuda][] 并安装： ```bash # install mish-cuda, if you use different pytorch version, you could try https://github.com/thomasbrandon/mish-cuda git clone https://github.com/JunnYu/mish-cuda cd mish-cuda python setup.py build install ``` 下载 [ScaledYOLOv4-large](https://github.com/WongKinYiu/ScaledYOLOv4/tree/yolov4-large): ```bash git clone -b yolov4-large https://github.com/WongKinYiu/ScaledYOLOv4 ``` ### 脚本依赖 ```bash conda activate scaled-yolov4 cd start-scaled-yolov4/ pip install -r scripts/requirements.txt ``` ## 模型准备 [yolov4-p5.pt]: https://drive.google.com/file/d/1aXZZE999sHMP1gev60XhNChtHPRMH3Fz/view?usp=sharing [yolov4-p6.pt]: https://drive.google.com/file/d/1aB7May8oPYzBqbgwYSZHuATPXyxh9xnf/view?usp=sharing [yolov4-p7.pt]: https://drive.google.com/file/d/18fGlzgEJTkUEiBG4hW00pyedJKNnYLP3/view?usp=sharing 下载官方的 [yolov4-p5.pt][], [yolov4-p6.pt][], [yolov4-p7.pt][] 权重文件到 `ScaledYOLOv4/weights/` 目录。 ## 现有模型测试 ### 准备 COCO 数据集，转成 YOLO 格式 下载 [COCO](http://cocodataset.org/) 数据集， ```bash coco2017 ├── annotations │ ├── instances_train2017.json │ └── instances_val2017.json ├── test2017 ├── train2017 └── val2017 ``` 转成 [YOLOv5 数据集结构](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data#3-organize-directories)， ```bash export COCO_DIR=~/datasets/coco2017 export OUTPUT_DIR=~/datasets/coco2017_yolov5 # train2017 训练集 # - 图片：目录软链到 images/ # - 标注：转换存储进 labels/*.txt # - 物体类型：全部记录进 *.names # - 图片列表：有物体标注的记录进 *.txt, 无的进 *.txt.ignored python scripts/coco2yolov5.py \ --coco_img_dir $COCO_DIR/train2017/ \ --coco_ann_file $COCO_DIR/annotations/instances_train2017.json \ --output_dir $OUTPUT_DIR # val2017 验证集 # - 物体类型：依照训练集的记录，保证顺序 python scripts/coco2yolov5.py \ --coco_img_dir $COCO_DIR/val2017/ \ --coco_ann_file $COCO_DIR/annotations/instances_val2017.json \ --output_dir $OUTPUT_DIR \ --obj_names_file $OUTPUT_DIR/train2017.names ``` 如下： ```bash coco2017_yolov5/ ├── images │ ├── train2017 -> /home/john/datasets/coco2017/train2017 │ └── val2017 -> /home/john/datasets/coco2017/val2017 ├── labels │ ├── train2017 │ └── val2017 ├── train2017.names ├── train2017.txt ├── train2017.txt.ignored ├── val2017.txt └── val2017.txt.ignored ``` `coco2017_yolov5` 软链到 `ScaledYOLOv4/` 目录，并添加 `ScaledYOLOv4/data/coco2017_yolov5.yaml` 文件，描述数据集： ```yaml # train and val datasets (image directory or *.txt file with image paths) train: ./coco2017_yolov5/images/train2017 val: ./coco2017_yolov5/images/val2017 test: ./coco2017_yolov5/images/val2017 # number of classes nc: 80 # class names names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush'] ``` ### 测试 YOLOv4-P5 ```bash cd ScaledYOLOv4 conda activate scaled-yolov4 pip install opencv-python pyyaml scipy tqdm python test.py \ --img 896 \ --conf 0.001 \ --batch 8 \ --device 0 \ --data data/coco2017_yolov5.yaml \ --weights weights/yolov4-p5.pt ``` 结果如下： ```bash Fusing layers... Model Summary: 331 layers, 7.07943e+07 parameters, 6.81919e+07 gradients Scanning labels coco2017_yolov5/labels/val2017.cache (4952 found, 0 missing, 48 empty, 0 duplicate, for 5000 images): 100%|█| 5000/5000 [00:00<00: Class Images Targets P R mAP@.5 mAP@.5:.95: 100%|█████████████| 625/625 [01:34<00:00, 6.64it/s] all 5e+03 3.68e+04 0.456 0.76 0.687 0.494 Speed: 14.3/1.5/15.8 ms inference/NMS/total per 896x896 image at batch-size 8 ``` 进行推断， ```bash python detect.py \ --img 896 \ --conf 0.5 \ --device 0 \ --weights weights/yolov4-p5.pt \ --source demo.jpg ``` 结果如下， ```bash Fusing layers... Model Summary: 331 layers, 7.07943e+07 parameters, 6.81919e+07 gradients image 1/1 /home/john/Codes/ScaledYOLOv4/demo.jpg: 768x896 1 cats, 1 dogs, Done. (0.029s) Results saved to inference/output Done. (0.133s) ```  ### 测试 YOLOv4-P7 ```bash python test.py \ --img 1536 \ --conf 0.001 \ --batch 6 \ --device 0 \ --data data/coco2017_yolov5.yaml \ --weights weights/yolov4-p7.pt ``` 结果如下： ```bash Fusing layers... Model Summary: 503 layers, 2.87475e+08 parameters, 2.7862e+08 gradients Scanning labels coco2017_yolov5/labels/val2017.cache (4952 found, 0 missing, 48 empty, 0 duplicate, for 5000 images): 100%|█| 5000/5000 [00:00<00: Class Images Targets P R mAP@.5 mAP@.5:.95: 100%|█████████████| 834/834 [06:57<00:00, 2.00it/s] all 5e+03 3.68e+04 0.435 0.804 0.719 0.531 Speed: 78.2/1.6/79.8 ms inference/NMS/total per 1536x1536 image at batch-size 6 ``` 进行推断， ```bash python detect.py \ --img 1536 \ --conf 0.5 \ --device 0 \ --weights weights/yolov4-p7.pt \ --source demo.jpg ``` 结果如下， ```bash Fusing layers... Model Summary: 503 layers, 2.87475e+08 parameters, 2.7862e+08 gradients image 1/1 /home/john/Codes/ScaledYOLOv4/demo.jpg: 1152x1536 1 cats, 1 dogs, 1 chairs, 1 couchs, 1 potted plants, Done. (0.079s) Results saved to inference/output Done. (0.282s) ```  ## 自定义数据集训练 ### 准备数据集 这里从 COCO 数据集拿出一个子集，作为自定义数据集的演示： ```bash cat <<EOF > subset.names cat dog EOF export COCO_DIR=~/datasets/coco2017 export OUTPUT_DIR=~/datasets/coco2017_yolov5_subset python scripts/coco2yolov5.py \ --coco_img_dir $COCO_DIR/train2017/ \ --coco_ann_file $COCO_DIR/annotations/instances_train2017.json \ --output_dir $OUTPUT_DIR \ --obj_names_file subset.names python scripts/coco2yolov5.py \ --coco_img_dir $COCO_DIR/val2017/ \ --coco_ann_file $COCO_DIR/annotations/instances_val2017.json \ --output_dir $OUTPUT_DIR \ --obj_names_file subset.names ``` `coco2017_yolov5_subset` 软链到 `ScaledYOLOv4/` 目录，并添加 `ScaledYOLOv4/data/coco2017_yolov5_subset.yaml` 文件，描述数据集： ```yaml # train and val datasets (image directory or *.txt file with image paths) train: ./coco2017_yolov5_subset/train2017.txt val: ./coco2017_yolov5_subset/val2017.txt test: ./coco2017_yolov5_subset/val2017.txt # number of classes nc: 2 # class names names: ['cat', 'dog'] ``` ### 准备参数文件 > 这里以 `YOLOv4-P6` 为例，`P5`, `P7` 一样。 复制 `ScaledYOLOv4/models/yolov4-p6.yaml` 到 `ScaledYOLOv4/models/coco2017_yolov5_subset/yolov4-p6.yaml` 文件，修改 `nc` 参数： ```bash nc: 2 # number of classes ``` ### 训练模型 ```bash conda activate scaled-yolov4 pip install tensorboard python train.py -h ``` 参数， ```bash optional arguments: -h, --help show this help message and exit --weights WEIGHTS initial weights path --cfg CFG model.yaml path --data DATA data.yaml path --hyp HYP hyperparameters path, i.e. data/hyp.scratch.yaml --epochs EPOCHS --batch-size BATCH_SIZE total batch size for all GPUs --img-size IMG_SIZE [IMG_SIZE ...] train,test sizes --rect rectangular training --resume [RESUME] resume from given path/last.pt, or most recent run if blank --nosave only save final checkpoint --notest only test final epoch --noautoanchor disable autoanchor check --evolve evolve hyperparameters --bucket BUCKET gsutil bucket --cache-images cache images for faster training --name NAME renames results.txt to results_name.txt if supplied --device DEVICE cuda device, i.e. 0 or 0,1,2,3 or cpu --multi-scale vary img-size +/- 50% --single-cls train as single-class dataset --adam use torch.optim.Adam() optimizer --sync-bn use SyncBatchNorm, only available in DDP mode --local_rank LOCAL_RANK DDP parameter, do not modify --logdir LOGDIR logging directory ``` 训练， ```bash python train.py \ --batch-size 2 \ --img 1280 1280 \ --data data/coco2017_yolov5_subset.yaml \ --cfg models/coco2017_yolov5_subset/yolov4-p6.yaml \ --weights '' \ --sync-bn \ --device 0,1 \ --name yolov4-p6 \ --epochs 100 ``` 信息如下：  如要恢复训练： ```bash python train.py \ --batch-size 2 \ --img 1280 1280 \ --data data/coco2017_yolov5_subset.yaml \ --cfg models/coco2017_yolov5_subset/yolov4-p6.yaml \ --weights 'runs/exp0_yolov4-p6/weights/last.pt' \ --sync-bn \ --device 0,1 \ --name yolov4-p6 \ --resume ``` #### 错误 `RuntimeError: main thread is not in main loop` ```bash Exception ignored in: <function Image.__del__ at 0x7f609bf9bd30> Traceback (most recent call last): File "/home/john/anaconda3/envs/scaled-yolov4/lib/python3.8/tkinter/__init__.py", line 4014, in __del__ self.tk.call('image', 'delete', self.name) RuntimeError: main thread is not in main loop Tcl_AsyncDelete: async handler deleted by the wrong thread Aborted (core dumped) ``` 如果发生此错误，可于 `train.py` `__main__` 修改 GUI 的 `backend`： ```bash if __name__ == '__main__': import matplotlib.pyplot as plt plt.switch_backend("agg") ``` ### 训练指标 训练完成后，内容如下： ```bash runs/exp0_yolov4-p6/ ├── events.out.tfevents.1610070159.john-ubuntu18.17638.0 ├── hyp.yaml ├── labels.png ├── opt.yaml ├── results.png ├── results.txt ├── test_batch0_gt.jpg ├── test_batch0_pred.jpg ├── train_batch0.jpg ├── train_batch1.jpg ├── train_batch2.jpg └── weights ├── best_yolov4-p6.pt ├── best_yolov4-p6_strip.pt ├── last_000.pt ├── last_yolov4-p6.pt └── last_yolov4-p6_strip.pt ``` - `labels.png`: 标注分布图 - `results.png`: 训练过程图 - `results.txt`: 训练过程日志 `results.png` 要训练完成后才有，如果训练过程中要查看，可用 `tensorboard`： ```bash $ tensorboard --logdir runs TensorFlow installation not found - running with reduced feature set. Serving TensorBoard on localhost; to expose to the network, use a proxy or pass --bind_all TensorBoard 2.4.0 at http://localhost:6006/ (Press CTRL+C to quit) ``` 打开 `http://localhost:6006/` 可见：  ### 测试模型 ```bash python test.py \ --img 1280 \ --conf 0.001 \ --batch 8 \ --device 0 \ --data data/coco2017_yolov5_subset.yaml \ --weights runs/exp0_yolov4-p6/weights/best_yolov4-p6_strip.pt ``` 进行推断， ```bash python detect.py \ --img 1280 \ --conf 0.5 \ --device 0 \ --weights runs/exp0_yolov4-p6/weights/best_yolov4-p6_strip.pt --source demo.jpg ```
