YOLOv4-pytorch trains its own data set

Introduction to YOLOv4-pytorch

Github address: argusswift/YOLOv4-pytorch: https://github.com/argusswift/YOLOv4-pytorch
This is a reproduction of the PyTorch version based on darknet's YOLOv4 structure, and also provides useful modules such as Mobilenetv3-YOLOv4, attentive YOLOv4, etc. Easy to operate and easy to read.

Environment configuration

operating environment

• Nvida GeForce RTX 2080TI
• CUDA10.0
• CUDNN7.0
• windows or linux
• python 3.6

Install dependencies

pip3 install -r requirements.txt --user

Preparation

Git clone YOLOv4

git clone github.com/argusswift/YOLOv4-pytorch.git

Prepare dataset

The model provides three supported data formats (PASCAL VOC, COCO, Customer).

Download the PascalVOC/MSCOCO 2017 dataset

PascalVOC：VOC 2012_trainval 、VOC 2007_trainval、VOC2007_test
MSCOCO 2017：train2017_img 、train2017_ann 、val2017_img 、val2017_ann 、test2017_img 、test2017_list

1. Put the data set in the directory, and update " DATA_PATH " in config/ yolov4_config.py to the data set location;
2. (For COCO dataset) Use utils/ coco_to_voc.py to convert COCO data type to VOC data type;
3. Use utils/ voc.py to convert PascalVOC's *.xml format to *.txt format or utils/coco.py to convert COCO's *.json format to *.txt format (Image_path xmin0, ymin0, xmax0, ymax0, class0 xmin1 ,ymin1,xmax1,ymax1,class1...).

Prepare your own dataset

Build your own dataset similar to the PascalVOC type:

- VOC
	- JPEGImage #原图片文件
	- Annotations #标注*.xml文件
	- ImageSets
		 - Main #训练、测试集
			 - train.txt
			 - test.txt

1. Put the picture in the JPEGImage folder, and put the annotation file in the Annotations folder;
2. Use utils/ xml_to_txt.py to write training set and test set to ImageSets/Main/*.txt ;
3. Use utils/ voc.py to convert PascalVOC's *.xml format to *.txt format or utils/coco.py to convert COCO's *.json format to *.txt format (Image_path xmin0, ymin0, xmax0, ymax0, class0 xmin1 ,ymin1,xmax1,ymax1,class1...);
4. Modify the NUM and CLASSES of Customer_DATA in config/ yolov4_config.py .

Customer_DATA = {
    
    
    "NUM": 2,  # your dataset number
    "CLASSES": [
        "name",
        "flag"
    ],  # your dataset class
}

Download the weights file

1. Darknet pre-training weight: YOLOv4 ;
2. Mobilenet pre-training weights: mobilenetv2 , mobilenetv3 (decompression password: args);
3. Create a new folder weight/, put the weight file into it;
4. Modify MODEL_TYPE in config/ yolov4_config.py .

MODEL_TYPE = {
    
    
    "TYPE": "YOLOv4"
}  # YOLO type:YOLOv4, Mobilenet-YOLOv4 or Mobilenetv3-YOLOv4

train

Modify the parameters in config/yolov4_config.py:

TRAIN = {
    
    
    "DATA_TYPE": "Customer",  # DATA_TYPE: VOC ,COCO or Customer
    "TRAIN_IMG_SIZE": 416,
    "AUGMENT": True,
    "BATCH_SIZE": 8,
    "MULTI_SCALE_TRAIN": False,
    "IOU_THRESHOLD_LOSS": 0.5,
    "YOLO_EPOCHS": 4000,
    "Mobilenet_YOLO_EPOCHS": 120,
    "NUMBER_WORKERS": 0,
    "MOMENTUM": 0.9,
    "WEIGHT_DECAY": 0.0005,
    "LR_INIT": 1e-4,
    "LR_END": 1e-6,
    "WARMUP_EPOCHS": 2,  # or None
}

Training instructions:

python -u train.py  --weight_path weight/yolov4.weights --gpu_id 0

or (nohup)

CUDA_VISIBLE_DEVICES=0 nohup python -u train.py  --weight_path weight/yolov4.weights --gpu_id 0 > nohup.log 2>&1 &

or (with --resume, automatically calling last.pt)

CUDA_VISIBLE_DEVICES=0 nohup python -u train.py  --weight_path weight/last.pt --gpu_id 0 > nohup.log 2>&1 &

test

picture test

for VOC dataset:
CUDA_VISIBLE_DEVICES=0 python3 eval_voc.py --weight_path weight/best.pt --gpu_id 0 --visiual $DATA_TEST --eval --mode det
for COCO dataset:
CUDA_VISIBLE_DEVICES=0 python3 eval_coco.py --weight_path weight/best.pt --gpu_id 0 --visiual $DATA_TEST --eval --mode det

video test

CUDA_VISIBLE_DEVICES=0 python3 video_test.py --weight_path best.pt --gpu_id 0 --video_path video.mp4 --output_dir --output_dir

problems encountered

1. evaluateer.py cannot find the *.xml annotation file

FileNotFoundError: [Errno 2] No such file or directory: '/home/my/YOLOv4-pytorch/data/VOC/Annotations\\18_3_dets0.xml'

报错原因：路径地址不正确
解决方法：
1.检查yolov4_config.py中DATA_PATH地址是否正确
2.evaluater.py，221 改为 self.val_data_path, "Annotations/" + "{:s}.xml"

References:
[1]: https://github.com/argusswift/YOLOv4-pytorch