基于tensorflow2的yolov4实现

• CSPDarknet53 + SPP + PAN (已实现)
• CIoU-loss (已实现)
• Mish激活函数 (已实现)
• Crossstage partial connections(CSP) (已实现)
• CutMix (未实现)
• Mosaic data augmentation (未实现)
• DropBlock regularization (未实现)
• Class label smoothing (未实现)
• Multiinput weighted residual connections(MiWRC) (未实现)
• CmBN (未实现)
• Self Adversarial Training (未实现)
• Eliminate gridsensitivity (未实现)
• Using multiple anchors for a single groundtruth (未实现)
• Cosine annealing scheduler (未实现)
• Optimal hyper-parameters (未实现)
• Random training shapes (未实现)
• Mish activation (未实现)
• SAM-block (未实现)
• DIoU-NMS (未实现)

素材标注使用Labelme https://github.com/tfwcn/labelme

python ./ai_api/yolo_v4/train_label.py --file_path "素材目录" --batch_size 8

启动服务：

python manage.py runserver 0.0.0.0:8080

浏览器打开：http://127.0.0.1:8080/static/object_detection/predict_image_read.html