Object Detection

This is the implementation of YOLOv1 for object detection in Tensorflow. It contains complete code for preprocessing, training and test. Besides, this repository is easy-to-use and can be developed on Linux and Windows.

YOLOv1 : Redmon, Joseph, et al. "You only look once: Unified, real-time object detection." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.

Getting Started

1 Prerequisites

Python3.6
Tensorflow
Opencv-python
Pandas

2 Define your class names

Download and unzip this repository.
cd ../YOLOv1/label
Open the label.txt and revise its class names as yours.

3 Prepare images

Copy your images and annotation files to directories ../YOLOv1/data/annotation/images and ../YOLOv1/data/annotation/images/xml respectively, where the annotations should be obtained by a graphical image annotation tool and saved as XML files in PASCAL VOC format.
cd ../YOLOv1/code
run python spilt.py
Then train and val images will be generated in ../YOLOv1/data/annotation/train and /YOLOv1/data/annotation/test directories, respectively.

4 Train model using Tensorflow

The model parameters, training parameters and eval parameters are all defined by parameters.py.
cd ../YOLOv1/code
run python train.py
The model will be saved in directory ../YOLOv1/model/checkpoint, and some detection results are saved in ../YOLOv1/pic.

5 Visualize model using Tensorboard

cd ../YOLOv1
run tensorboard --logdir=model/
Open the URL in browser to visualize model.

Examples

Here are two successful detection examples in my dataset:

swhsiang/tensorflow-MobilenetV2-YOLOv1