We will do the work in this directory. NOTE Change the name of the file you unzipped to models. NOTE Can be duplicates in command below. Then run the following commands in models/research directory to run the setup.py Your dataset must be in voc format and each image must have its own tag file (with an .xml

A guide showing how to train TensorFlow Lite object detection models and run them on Android, the Raspberry Pi, and more! TensorFlow Lite is an optimized framework for deploying lightweight deep learning models on resource-constrained edge devices. TensorFlow Lite models have faster inference time and require less processing power, so they can be used to obtain faster performance in realtime applications. 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 Android phones or the Raspberry Pi. The guide is broken into three major portions. Each portion will have its own dedicated README file in this repository. This repository also contains Python code for running the newly converted TensorFlow Lite model to perform detection on images, videos, or webcam feeds. I used TensorFlow v1.13 while creating this guide, because TF v1.13 is a stable version that has great support from Anaconda. I will periodically update the guide to make sure it works with newer versions of TensorFlow.

Deep learning provides yet another way to solve the Where's Wally puzzle problem. But unlike traditional image processing computer vision methods, it works using only a handful of labelled examples that include the location of Wally in an image. Final trained model with evaluation images and detection scripts is published on my Github repo. This post describes the process of training a neural network using Tensorflow Object Detection API and using a Python script built around it to find Wally. Before starting, make sure to install Tensorflow Object Detection API as per the instructions.

Deep learning provides yet another way to solve the Where's Wally puzzle problem. But unlike traditional image processing computer vision methods, it works using only a handful of labelled examples that include the location of Wally in an image. Final trained model with evaluation images and detection scripts is published on my Github repo. This post describes the process of training a neural network using Tensorflow Object Detection API and using a Python script built around it to find Wally. Before starting, make sure to install Tensorflow Object Detection API as per the instructions.