All Projects → leeyoshinari → YOLO_tiny

leeyoshinari / YOLO_tiny

Licence: MIT License
The implementation of YOLO_v1 with TensorFlow

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YOLO_tiny

This implementation of YOLO with TensorFlow.

Installation

  1. Clone YOLO_tiny repository

    $ git clone https://github.com/leeyoshinari/YOLO_tiny.git
    $ cd YOLO_tiny
  2. Download Pascal VOC2007 dataset, and put the dataset into data/Pascal_voc.

    if you download other dataset, you also need to modify file paths.

  3. Download weights file yolo_tiny, and put weight file into data/output.

    Or you can also download my training weights file YOLO_tiny.

  4. Modify configuration into yolo/config.py.

  5. Training

    $ python train.py
  6. Test

    $ python test.py

Training on Your Own Dataset

To train the model on your own dataset, you should need to modefy:

  1. Put all the images into the Images folder, put all the labels into the Labels folder. Select a part of the image for training, write this part of the image filename into train.txt, the remaining part of the image filename written in test.txt. Then put the Images, Labels, train.txt and test.txt into data/dataset. Put weight file in data/output.

  2. config.py modify the CLASSES.

  3. train.py replacefrom utils.pascal_voc import pascal_voc with from utils.preprocess import preprocess, and replace pascal = pascal_voc() with pascal = preprocess().

Requirements

  1. Tensorflow
  2. OpenCV
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