ronen-halevy/myolov3

TensorFlow2.x-YOLOv3

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A minimal tensorflow implementation of YOLOv3, with support for training, inference and evaluation.

Installation

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Install requirements and download pretrained weights

$ pip3 install -r ./docs/requirements.txt
$ wget https://pjreddie.com/media/files/yolov3.weights

Quick start

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In this part, we will use pretrained weights to make predictions on both image and video.

$ python image_demo.py

Train yymnist

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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

Citation

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@Github_Project{TensorFlow2.0-Examples,
  author       = YunYang1994,
  email        = www.dreameryangyun@sjtu.edu.cn,
  title        = "YOLOv3: An Incremental Improvement",
  url          = https://github.com/YunYang1994/TensorFlow2.0-Examples,
  year         = 2019,
}

myolov3