Scaled-YOLOv4 快速开始，训练自定义数据集

代码： https://github.com/ikuokuo/start-scaled-yolov4

Scaled-YOLOv4

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
Nvidia Driver
- 驱动安装，可见 Ubuntu 初始配置 - Nvidia 驱动

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：

pytorch 等版本请对照表 torch, torchvision, python
cudatoolkit 版本请对照表 CUDA Toolkit and Compatible Driver Versions
conda 下载过慢，可尝试配置国内镜像源

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

│   └── 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：

# 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 到 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：

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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