An experiment of transferring backbone of yolov3 into mobilenetv3 which is implemented by TF/Keras and inspired by qqwweee/keras-yolo3 and xiaochus/MobileNetV3
Training
Generate your own annotation file and class names file. One row for one image; Row format: image_file_path box1 box2 ... boxN; Box format: x_min,y_min,x_max,y_max,class_id (no space). For VOC dataset, try python voc_annotation.py Here is an example:
Modify train.py and start training. python train.py
If you want to train from scratch ,set load_pretrained=False ;if training was interupted , you can set load_pretrained=True and load weights from weights_path ,then restart training.
positional arguments: --input Video input path --output Video output path
optional arguments: -h, --help show this help message and exit --model MODEL path to model weight file, default model_data/yolo.h5 --anchors ANCHORS path to anchor definitions, default model_data/yolo_anchors.txt --classes CLASSES path to class definitions, default model_data/coco_classes.txt --gpu_num GPU_NUM Number of GPU to use, default 1 --image Image detection mode, will ignore all positional arguments