(Latest Version TensorFlow App) — Train a Deep Learning model for custom image classification using Teachable Machine, convert it to a TFLite model, and finally deploy it on mobile devices using the sample TFLite image classification app from TensorFlow’s GitHub. IMPORTANT THIS IS FOR THE LATEST VERSION OF THE TENSORFLOW APP ON GITHUB. USE THE FOLLOWING…

Train a Deep Learning model for custom image classification using Teachable Machine, convert it to a TFLite model, and finally deploy it on mobile devices using the sample TFLite image classification app from TensorFlow’s GitHub. IMPORTANT THIS IS FOR AN OLDER JAVA VERSION OF THE TENSORFLOW APP. YOU CAN SWITCH TO…

(For a custom object) — Objective: Train a Deep Learning model for custom object image classification using Teachable Machine, test it online, and finally export it into different formats to use in your projects. Roadmap Collect the dataset of images. Open Teachable-Machine’s image project Train the model. Evaluate the model. Export the model. Image classification or…

Run YOLOv4 inference using the OpenCV-DNN-CUDA module on Linux. In the previous blog “Setup OpenCV-DNN module with CUDA backend support (For Linux)”, we built the OpenCV-DNN module with CUDA backend support on Linux. (I have Ubuntu 18.04). We can use this OpenCV module to run inference on object detection models. The OpenCV-DNN module built with CUDA backend support gives…

In this tutorial, we will be building OpenCV from source with CUDA backend support (OpenCV-DNN-CUDA module). IMPORTANT: The OpenCV-DNN module only supports inference so although you will get much faster inference out of it, the training however will be the same as was for the OpenCV we set up without CUDA backend support. STEPS 1. Install CUDA & cuDNN. 2. Install the necessary Ubuntu packages if not…

Run YOLOv4 inference using the OpenCV-DNN-CUDA module on Windows. In the previous blog “Setup OpenCV-DNN module with CUDA backend support (For Windows)”, we built the OpenCV-DNN module with CUDA backend support on Windows. We can use this OpenCV module to run inference on object detection models. The OpenCV-DNN module built with CUDA backend support gives much faster inference as…

In this tutorial, we will be building OpenCV from source with CUDA backend support (OpenCV-DNN-CUDA module). IMPORTANT: The OpenCV-DNN-CUDA module only supports inference. So although you will get much faster inference out of it, the training however will be the same as for the OpenCV we set up without CUDA backend support. STEPS 1. Install CUDA & cuDNN. 2. Install CMake GUI. 3. Install Anaconda Individual edition.

Using TensorFlow Object Detection API (For old version TF app) — Training a Deep Learning model for custom object detection using TensorFlow 2.x in Google Colab and converting it to a TFLite model for deploying on mobile devices like Android, iOS, Raspberry Pi, IoT devices using the sample TFLite object detection app from TensorFlow’s GitHub. IMPORTANT: This tutorial is for TensorFlow 2.5…

Using TensorFlow Object Detection API — My Youtube Video on this! In this tutorial, I will be training a Deep Learning model for custom object detection using TensorFlow 2.x on Google Colab. Following is the roadmap for it. Roadmap Collect the dataset of images and label them to get their XML files. Install the TensorFlow Object Detection API. Generate the TFRecord files required for training. (need generate_tfrecord.py script and CSV files for this)