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 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