基于TensorFlow2.0 训练YOLOV3模型和Wider Face 数据集进行人脸检测（python源码+说明）.zip

# TensorFlow2.x-YOLOv3 A minimal tensorflow implementation of YOLOv3, with support for training, inference and evaluation. ## Installation Install requirements and download pretrained weights ``` $ pip3 install -r ./docs/requirements.txt $ wget https://pjreddie.com/media/files/yolov3.weights ``` ## Quick start In this part, we will use pretrained weights to make predictions on both image and video. ``` $ python image_demo.py $ python video_demo.py # if use camera, set video_path = 0 ```  ## Train yymnist Download [yymnist](https://github.com/YunYang1994/yymnist) dataset and make data. ``` $ git clone https://github.com/YunYang1994/yymnist.git $ python yymnist/make_data.py --images_num 1000 --images_path ./data/dataset/train --labels_txt ./data/dataset/yymnist_train.txt $ python yymnist/make_data.py --images_num 200 --images_path ./data/dataset/test --labels_txt ./data/dataset/yymnist_test.txt ``` Open `./core/config.py` and do some configurations ``` __C.YOLO.CLASSES = "./data/classes/yymnist.names" ``` Finally, you can train it and then evaluate your model ``` $ python train.py $ tensorboard --logdir ./data/log $ python test.py $ cd ../mAP $ python main.py # Detection images are expected to save in `YOLOV3/data/detection` ``` Track training progress in Tensorboard and go to http://localhost:6006/ ``` $ tensorboard --logdir ./data/log ```  | train |test| |---|--- |||