How to convert your YOLOv4 weights to TensorFlow 2.2.0?

Another post starts with you beautiful people!
Thank you all for your overwhelming response in my last two posts about the YOLOv4. It is quite clear that my beloved aspiring data scientists are very much curious to learn state of the art computer vision technique but they were not able to achieve that due to the lack of proper guidance. Now they have learnt exact steps to use a state of the art object detection and recognition technique from my last two posts. If you are new to my blog and want to use YOLOv4 in your project then please follow below two links-

In my last post we have trained our custom dataset to identify eight types of Indian classical dance forms. After the model training we have got the YOLOv4 specific weights file as 'yolo-obj_final.weights'. This YOLOv4 specific weight file cannot be used directly to either with OpenCV or with TensorFlow currently because in the latest release of YOLO a new activation function-Mish is introduced. With current version of OpenCV and TF using YOLOv4 directly is not possible. In this blog post I am going to share you my jupyter notebook code for converting YOLOv4 weights to TF2.2.0.

TensorFlow(TF) is an easy to use framework in production. If we have a TF model we can deploy it in a web application, mobile application or embedded devices. Since I am a Keras guy, I prefer to use Keras API with TensorFlow backend in my projects :) . I have taken the help from the older post of Jason Brownlee PhD and modified the code with YOLOv4 related changes. You can fork or download the notebook from my github repository You can gone through the notebook and if you want to update or add any thing to it, send me a PR in github :)

In the first cell of the notebook I have imported required libraries and also checking the TF and Keras version-

So we have the latest keras and TF version currently available in colab notebook. In next cell, I have defined the newly added activation function Mish as below-

And in third cell, this activation function is added to the convolution layer blocks-

In further cells, I have defined the YOLOv4 architecture as per keras & TF and then other helper functions like drawing bounding boxes, removing unwanted bounding boxes and showing the detecting in an image. Let me show you the screenshots where I have converted a pretrained YOLOv4 weights to a TF model file-

After running above cells you will have a .h5 format file which is infact a TensorFlow model file. Now we will verify that our converted model file is working same as YOLOv4 weights or not. To do so let's import this TensorFlow model file-

With this code our .h5 file is successfully loaded as keras model. Let's define a list of objects on which original YOLOv4 weights was trained and also a function to read those object labels-

Next step is to define a function to load and preprocess an image for keras-

Let's apply this preprocessing function to an image in this way-

Next, we will use the predict() function to make the prediction on our image-

As a last step we will draw the bounding boxes and predicted object on that bounding boxes in our picture. All required processing for this step is already done in the notebook.Once we run the following cell-

Along with predicted object with accuracy, it will give you following like image-

The above image and result confirms that our conversion code is working as expected. That's it for today guys! Go ahead- Fork or download the complete notebook from my github repo, try it yourself, explore it with your custom YOLOv4 weights file and see how can you make this notebook more useful. In my next blog post I will share my new learning with you all. Till then Go chase your dreams, have an awesome day, make every second count and see you later in my next post.