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Scaled-YOLOv4 开始

Scaled-YOLOv4#

环境准备#

基础环境#

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 -yconda activate scaled-yolov4
# 安装 PyTorch with CUDAconda 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.runsudo 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-cudagit clone https://github.com/JunnYu/mish-cudacd mish-cudapython 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/coco2017export 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.ignoredpython 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/train2017val: ./coco2017_yolov5/images/val2017test: ./coco2017_yolov5/images/val2017
# number of classesnc: 80
# class namesnames: ['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-yolov4pip 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 gradientsScanning 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       0.494Speed: 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 gradientsimage 1/1 /home/john/Codes/ScaledYOLOv4/demo.jpg: 768x896 1 cats, 1 dogs, Done. (0.029s)Results saved to inference/outputDone. (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 gradientsScanning 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       0.531Speed: 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 gradientsimage 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/outputDone. (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.namescatdogEOF
export COCO_DIR=~/datasets/coco2017export 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.txtval: ./coco2017_yolov5_subset/val2017.txttest: ./coco2017_yolov5_subset/val2017.txt
# number of classesnc: 2
# class namesnames: ['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.yamlScaledYOLOv4/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-yolov4pip 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 loopTcl_AsyncDelete: async handler deleted by the wrong threadAborted (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 runsTensorFlow installation not found - running with reduced feature set.Serving TensorBoard on localhost; to expose to the network, use a proxy or pass --bind_allTensorBoard 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