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Scaled-YOLOv4 快速开始,训练自定义数据集

2021-01-09 19:28 330 查看

Scaled-YOLOv4

环境准备

基础环境

  • Nvidia Driver

    开发环境

    下载并安装 Anaconda ,之后于 Terminal 执行:

    # 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:

    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:

    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。

    下载 mish-cuda 并安装:

    # 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:

    git clone -b yolov4-large https://github.com/WongKinYiu/ScaledYOLOv4

    脚本依赖

    conda activate scaled-yolov4
    
    cd start-scaled-yolov4/
    pip install -r scripts/requirements.txt

    模型准备

    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

    下载 COCO 数据集,

    coco2017
    ├── annotations
    │   ├── instances_train2017.json
    │   └── instances_val2017.json
    ├── test2017
    ├── train2017
    └── val2017

    转成 YOLOv5 数据集结构

    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:

    coco2017_yolov5/
    ├── images
    │   ├── train2017 -> /home/john/datasets/coco2017/train2017
    │   └── v
    103c
    al2017 -> /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:

    # 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

    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:

    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,

    python detect.py \
    --img 896 \
    --conf 0.5 \
    --device 0 \
    --weights weights/yolov4-p5.pt \
    --source demo.jpg

    结果如下,

    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

    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:

    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,

    python detect.py \
    --img 1536 \
    --conf 0.5 \
    --device 0 \
    --weights weights/yolov4-p7.pt \
    --source demo.jpg
    

    结果如下,

    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:

    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:

    # 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
    ScaledYOLOv4/models/coco2017_yolov5_subset/yolov4-p6.yaml
    文件,修改
    nc
    参数:

    nc: 2  # number of classes

    训练模型

    conda activate scaled-yolov4
    pip install tensorboard
    
    python train.py -h

    参数,

    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,

    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:

    < 1c39 p>

    如要恢复训练:

    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

    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

    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:

    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

    $ 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

    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,

    python detect.py \
    --img 1280 \
    --conf 0.5 \
    --device 0 \
    --weights runs/exp0_yolov4-p6/weights/best_yolov4-p6_strip.pt
    --source demo.jpg

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