Hello,
I have a Keras model (model.h5) of a tiny YOLOv3 object-detection model that results in two outputs, conv2d_2 and conv2d_5 (convolution layers), after implementing the following tutorial: GitHub - david8862/keras-YOLOv3-model-set: end-to-end YOLOv4/v3/v2 object detection pipeline, implemented on tf.keras with different technologies
In the evaluation/testing script provided, eval.py, every image is fed into an internal method of the Keras model, .predict(), and two Numpy arrays are returned. I would like to understand how these arrays are produced (what the method takes as input data from the model) and what exactly the data that is in them means.
I tried to go through the source code for the .predict() method (https://github.com/tensorflow/tensorflow/blob/v2.5.0/tensorflow/python/keras/engine/training.py#L1593-L1745), but I honestly couldn’t really grasp how it works or what it’s doing exactly.
I would appreciate any help I can get with this.
Thanks,
Ahmad