Abstract
Some guys may know how to use Darknet, some other guys may be professionals in Tensorflow-Keras. In my experience of using both frameworks, Darknet was created for object detection, thus, it should be optimized and produce more favorable results than that of Tensorflow-Keras in this task. It is like a guy who knows one thing deeply and another guy who knows things extensively. In this scenario, Darknet is the first guy who knows deeply about YOLO. However, Darknet was not designed for users to customize too much! For example, you can change the network architecture, hyperparameters, but you can not or it is too hard to do some high-level learning methods like knowledge distillation, hint-based learning, or feature map analysis, but Tensorflow-Keras can. To take advantage of the beautiful values from both frameworks, I introduce Darkeras, a tool for converting Darknet pre-trained weights and executing object detection on Tensorflow-Keras.
The code of this work is publicly available at https://github.com/tranleanh/darkeras-yolov4.
Email: tranleanh.nt@gmail.com
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