[P] TensorFlow 2.0 Implementation of Yolo V3 Object Detection Network
Hey reddit r/ml, I am sharing my implementation of YoloV3 in TensorFlow 2.0 alpha
There is a lot of buzz around TensorFlow 2.0 with tons of blog posts and tutorial. But I haven’t found a complete example that uses all the latest features and best practices brought by TF2. This project is created with the goal of being clean, efficient and complete with zero legacy debts.
Some of the key features include:
The YoloV3 implementation is mostly referenced from the origin paper, original darknet with inspirations from many existing code written in PyTorch, Keras and TF1 (I credited them at the end of the README). I tried to fixed all the inconsistency, incompleteness and minor errors existing in other repos here. The project works with both YoloV3 and YoloV3-Tiny and is compatible with pre-trained darknet weights.
Example of detection output:
This project has been quite a great learning experience for me. After working with TF1 and then Keras and then PyTorch, coming back to TensorFlow 2.0 feels very refreshing and enjoyable. TF2 will definitely rise and shine in the coming months following the official GA release.