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Author: techzizou007
Build Android app for custom object image classification
(Using Google Colab) Objective: Build Android app for custom object image classification using Google Colab. Train a Machine Learning model for custom object image classification using TensorFlow 2.x,
YOLOv4-darknet installation and usage on your system (Windows & Linux)
CONTENTS SECTION (A) Installing on Windows SECTION (B) YOLO Usage on Windows SECTION (C) Installing on Linux SECTION (D) YOLO Usage on Linux (A) Installing YOLOv4 on Windows
Install CUDA and CUDNN on Windows & Linux
CONTENTS CUDA & CUDNN FOR WINDOWS CUDA & CUDNN FOR LINUX CUDA & CUDNN FOR WINDOWS STEP 1) Download and install CUDA Toolkit Go to https://developer.nvidia.com/cuda-downloads to download
Install OpenCV-DNN with Cuda backend in Google Colab
First, let’s check the default OpenCV version on Colab. Let’s Begin Step 1) Clone the latest OpenCV build from GitHub Step 2) Next, run the cmake command Set
Build Android app for custom object detection (TF 2.x)
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Training a model for custom object detection (TF 1.x)
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YOLOv4 VS YOLOv4-tiny
Training custom YOLO detectors for Object Detection What is YOLO? YOLO stands for You Only Look Once. YOLO is a state-of-the-art, real-time object detection system. It was developed by
Train a custom YOLOv4-tiny detector using Google Colab
(Tutorial for beginners) In this tutorial, we will be training a custom detector for mask detection using YOLOv4-tiny and Darknet HOW TO BEGIN? Open my Colab notebook on your browser. Click on File in the
Train a custom YOLOv4 detector using Google Colab
(Tutorial for beginners) In this tutorial, we will be training our custom detector for mask detection using YOLOv4 and Darknet HOW TO BEGIN? Open my Colab notebook on your browser. Click on File in
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