Learn how to install, load custom data, train, and infer with your custom TensorFlow 2 Object Detection model to detect any object in the world.

With the recent release of the TensorFlow 2 Object Detection API, it has never been easier to train and deploy custom state of the art object detection models with TensorFlow. To build a custom model you can leverage your own custom dataset to detect your own custom objects: foods, pets, mechanical parts, and more.

In this blog and TensorFlow 2 Object Detection Colab Notebook, we walk through how you can train your own custom object detector in minutes, by changing a single line of code for your dataset import.

Train your custom object detector with the TensorFlow2 Object Detection API

In order to train our custom object detector with the TensorFlow 2 Object Detection API we will take the following steps in this tutorial:

• Discuss the TensorFlow 2 Object Detection API
• Acquire Labeled Object Detection Data
• Install TensorFlow 2 Object Detection Dependencies
• Download Custom TensorFlow 2 Object Detection Dataset
• Write Custom TensorFlow 2 Object Detection Training Configuration
• Train Custom TensorFlow 2 Object Detection Model
• Export Custom TensorFlow 2 Object Detection Weights
• Use Trained TensorFlow 2 Object Detection For Inference on Test Images

Resources included in this tutorial:

• TensorFlow 2 Object Detection Colab Notebook
• Public Blood Cell Object Detection Dataset
• TF2 OD GitHub Repository

Let’s get started!

#object-detection #tensorflow #machine-learning