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New to machine learning so looking for some direction how to get started. The end goal is to be able to train a model to count the number of objects in an image using Tensorflow. My initial focus will be to train the model to count one specific type of object. So let's say I take coins. I will only train the model to count coins. Not worried about creating a generic counter for all different types of objects. I've only done Google's example of image classification of flowers and I understand the basics of that. So looking for clues on how to get started. Is this an image classification problem and I can use the same logic as the flowers...etc?

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Accurately counting objects instances in a given image or video frame is a hard problem to solve in machine learning. A number of solutions have been developed to count people, cars and other objects and none of them is perfect. Of course, we are talking about image processing here, so a neural network seems to be a good tool for the job.

The TensorFlow Object Counting API is an API for the TensorFlow. It is an open-source framework built on top of TensorFlow and Keras that makes it easy to develop object counting systems. Here is a cool implementation for the training of a model to count the number of objects in an image using Tensorflow.

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