Jun 16, 2021 · Training a custom object detection model and deploying it to an Android app has become super easy with TensorFlow Lite. We released a learning pathway that teaches you step-by-step how to do it. In the video, you can learn the steps to build a custom object detector: Prepare the training data.

Aug 30, 2024 · In this colab notebook, you'll learn how to use the TensorFlow Lite Model Maker library to train a custom object detection model capable of detecting salads within images on a mobile device. The Model Maker library uses transfer learning to simplify the process of training a TensorFlow Lite model using a custom dataset.

This is a camera app that continuously detects the objects (bounding boxes and classes) in the frames seen by your device's back camera, with the option to use a quantized MobileNet SSD, EfficientDet Lite 0, EfficientDet Lite1, or EfficientDet Lite2 model trained on the COCO dataset.

Feb 13, 2025 · GitHub: TensorFlow Lite Object Detection. This notebook uses the TensorFlow 2 Object Detection API to train an SSD-MobileNet model or EfficientDet model with a custom dataset and...

This guide provides step-by-step instructions for how train a custom TensorFlow Object Detection model, convert it into an optimized format that can be used by TensorFlow Lite, and run it on edge devices like the Raspberry Pi.

If you just want to do general object detection without training your own model, check out this codelab to learn how to use the ML Kit Object Detection and Tracking API to build an object...

Tensorflow Lite Model Maker currently supports 5 different object detection models (EfficientDet-Lite [0-4]). All of them are derived from the EfficientDet architecture. The main differences between the models are their size and latency. * Size of the integer quantized models. ** Latency measured on Pixel 4 using 4 threads on CPU.

Apr 12, 2024 · Contrary to image classification models that classify an input image into one or more different categories, object detection models are designed to identify target objects and provide you with a bounding box around them (to track its location). With that context established, let’s jump into how to implement these models in a Python setting.

Jan 11, 2025 · By the end of this guide, you'll know how to set up, train, and deploy a real-time object detection model using TensorFlow Lite. Let's get started! TensorFlow Lite is Google's lightweight solution for running machine learning models on mobile and embedded devices.

In this colab notebook, you'll learn how to use the TensorFlow Lite Model Maker to train a custom object detection model to detect Android figurines and how to put the model on a Raspberry...