TensorFlow 2.0 Object Detection API Tutorial

TensorFlow 2.0 Object Detection API Tutorial

In this tutorial, we’ll be covering how to use the TensorFlow 2.0 Object Detection API to build a custom object detection model that can identify and track any objects you want.

Explore our new video:

Introduction

Welcome to the TensorFlow 2.0 Object Detection API tutorial. In this tutorial, you will learn how to use the TensorFlow 2.0 Object Detection API to perform object detection in images. The Object Detection API provides a set of models that can be used to perform a variety of tasks, such as identifying objects in images, making predictions about objects in images, and providing bounding boxes around objects in images.

What’s new in TensorFlow 2.0 Object Detection API

In this tutorial, we will explore the changes and additions in the TensorFlow 2.0 Object Detection API.

TensorFlow 2.0 Object Detection API is now available as a part of TensorFlow 2.0. The biggest change is that the API is now built on top of Keras, making it easier to work with and more flexible. Other changes include:
-A newHead object detection model based on MobileNetV2
-Support for TensorFlow Lite
-Experimental support for running object detection on GPU
-A new version of the criterion that improves accuracy

Getting started

Welcome to the TensorFlow 2.0 Object Detection API tutorial. This tutorial will take you through the steps required to train an object detection model using the TensorFlow 2.0 Object Detection API. The API is an open source framework built on top of TensorFlow that makes it easy to construct, train, and deploy object detection models.

This tutorial is geared towards beginners who have little to no experience with TensorFlow or the Object Detection API. By the end of this tutorial, you will have trained a model that can detect objects in images.

Creating an Object Detection Pipeline

Creating an Object Detection Pipeline
In this tutorial, we’ll be covering how to train your own object detection model on an image dataset, using the TensorFlow 2.0 Object Detection API.

We’ll be using the Oxford-IIIT Pets dataset, which features 37 different breeds of cats and dogs, including both common and rare breeds. The dataset has been carefully annotated so that each image is correctly labelled with the correct breed of animal.

We’ll be using Google Colab for this tutorial, which provides free GPU access. You can clone this notebook and run it on your own machine if you prefer, but you’ll need to install TensorFlow 2.0 and the Object Detection API yourself.

Let’s get started!

Training an Object Detection Model

In this tutorial, you will learn how to train an object detection model using the TensorFlow 2.0 Object Detection API.

You will need a computer with internet access and an up-to-date web browser. You will also need to have TensorFlow 2.0 and the Object Detection API installed. You can find instructions for doing this in the README for the Object Detection API repository on GitHub.

Once you have everything set up, you will need some training data. The Object Detection API makes it easy to use your own training data by providing a simple way to upload images and annotations in the Pascal VOC format. Alternatively, you can download pre-trained models that were trained on the COCO dataset or the Open Images dataset.

Once you have your training data, you are ready to start training your model!

Evaluating an Object Detection Model

It is important to evaluate your object detection model before you use it in production. This will help you gauge its accuracy and determine if it is suitable for your needs.

There are a few different ways to evaluate an object detection model. One way is to measure the Intersection over Union (IoU) between the predicted bounding boxes and the ground truth bounding boxes. This metric can be used to compare different object detection models.

Another way to evaluate an object detection model is to measure the Average Precision (AP). The AP is computed by first drawing a precision-recall curve and then calculating the area under the curve. The AP is a more comprehensive metric than the IoU and is often used to compare different object detection models.

In this tutorial, you will learn how to evaluate an object detection model using the TensorFlow 2.0 Object Detection API. You will use a validation dataset to evaluate your model and calculate the AP.

Deploying an Object Detection Model

TensorFlow’s Object Detection API is an open-source framework built on top of TensorFlow that makes it easy to construct, train, and deploy object detection models.

In this tutorial, we will learn how to use the Object Detection API to deploy a pre-trained model that will be able to detect objects in an image. We will also learn how to use the Visual Object Tagging Tool (VoTT) to label images for training. By the end of this tutorial, you should be able to take an image like the one above and draw bounding boxes around all of the objects in the image using just a few lines of code.

Object Detection Use Cases

There are many potential use cases for object detection. Some examples include:
-Autonomous vehicles
-Security and surveillance
-Industrial inspection
-Robotics
-Package delivery

Conclusion

In this tutorial, we’ve gone over the TensorFlow 2.0 Object Detection API: how to install it, set it up, and utilize it. We also discussed some of its key features, such as the Model Zoo and AutoML. Finally, we went through a complete example of training and deploying a custom object detection model.

Keyword: TensorFlow 2.0 Object Detection API Tutorial

Leave a Comment

Your email address will not be published. Required fields are marked *

Scroll to Top