Models Research Object Detection Colab Tutorials Object Detection

There was an error while loading. Please reload this page. Welcome to the TensorFlow Hub Object Detection Colab! This notebook will take you through the steps of running an "out-of-the-box" object detection model on images. This collection contains TF2 object detection models that have been trained on the COCO 2017 dataset. Here you can find all object detection models that are currently hosted on tfhub.dev.

Run the following cell to create some utils that will be needed later: To visualize the images with the proper detected boxes, keypoints and segmentation, we will use the TensorFlow Object Detection API. To install it we will clone the repo. Now we can import the dependencies we will need later Object detection is a fascinating area of computer vision that has seen tremendous progress in recent years thanks to advances in deep learning. The ability for AI systems to localize and identify objects in images and video has unlocked a wide range of applications, from self-driving cars to cashierless stores to smart security cameras.

One of the most impactful developments in object detection was the introduction of the YOLO (You Only Look Once) model in 2015. YOLO represented a totally new approach compared to previous methods. Rather than using a complex pipeline of separate components, YOLO framed object detection as a single regression problem, taking an input image and learning to directly output bounding box coordinates and class probabilities. This simplified architecture allowed YOLO to be incredibly fast, running in real-time at up to 45 frames per second. And while the initial version lagged behind in accuracy compared to slower two-stage detectors, later iterations refined the model to achieve state-of-the-art results while still maintaining high speed. At a high level, YOLO divides up the input image into a grid and for each grid cell predicts a fixed number of bounding boxes, confidence for those boxes, and class probabilities.

The version of YOLO we‘ll be using is YOLOv3, which uses a 106 layer fully convolutional underlying architecture and predicts an objectness score for each bounding box using logistic regression. Here‘s a visualization of the YOLO model in action: In this tutorial, you will learn how to train a custom object detection model easily with TensorFlow object detection API and Google Colab's free GPU. Annotated images and source code to complete this tutorial are included. Otherwise, let's start with creating the annotated datasets. In my case, I use my iPhone to take those photos, each come with 4032 x 3024 resolution, it will overwhelm the model if we use that as direct input to the model.

Instead, resize those photos to uniformed size (800, 600) can make training and inference faster. You can use the resize_images.py script in the repository to resize your images. This repository provides a complete pipeline for training your own custom object detection model using the TensorFlow Object Detection API. The process is simplified using Google Colab, making it easy to run on free GPU hardware without any local setup. This project demonstrates the full lifecycle of a custom object detection task — from dataset preparation to training, exporting, and performing inference on new images. All major steps are integrated into a Colab notebook.

Data Preparation • Annotate your images using a tool like LabelImg. • Save the .xml annotation files in the same folders as the corresponding images. • Convert the annotated data into TFRecord format — a TensorFlow-compatible format for training. Model Configuration • Choose a pre-trained model from the TensorFlow Model Zoo. • Edit the model’s pipeline.config to: • Set the paths for TFRecord files • Link your label_map.pbtxt • Adjust training parameters (e.g., batch size, learning rate) Model Training • Use the tensorflow_object_detection_training_colab.ipynb notebook.

• It installs dependencies, sets up the environment, and initiates training. • Training can be monitored in real time (e.g., loss values, step count). Google Colab is a free online platform to execute machine learning experiments, and TensorFlow is an open-source software library for numerical computation using data flow graphs. With TensorFlow, you can create and train machine learning models, including object detection models. If you want to use TensorFlow to perform object detection tasks in Google Colab, this tutorial will walk you through the steps involved in setting up your environment. By the end of this tutorial, you’ll have a fully functioning environment for object detection experiments.

Object detection is a computer vision task that involves identifying and locating objects of interest within an image or video frame. It has numerous practical applications, from self-driving cars to facial recognition and wildlife monitoring. TensorFlow, with its vast ecosystem of pre-trained models and tools, simplifies the process of developing and training object detection models. Before we dive into TensorFlow Object Detection in Colab, make sure you have the following prerequisites: Now that you have these prerequisites, let’s get started.

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