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.
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: