Faster R-CNN on custom dataset Using Pytorch

Step-by-Step Guide: Creating, Training, and Inference with Faster R-CNN on a Custom Dataset

GitHub: https://github.com/AarohiSingla/Faste...

If you have any questions or need further assistance, feel free to contact me at [email protected].

In this video, I walk you through the entire process of using Faster R-CNN, one of the most popular object detection models:
1️⃣ Dataset Preparation: Learn how to create and structure a dataset in the COCO format, including images and annotations.
2️⃣ Model Training: Follow along as we train a Faster R-CNN model with a ResNet-50 FPN backbone on a custom dataset containing classes like chairs, tables, and humans.
3️⃣ Inference: See the trained model in action as we perform object detection on new images.

This tutorial is perfect for beginners and professionals who want a practical, hands-on guide to building robust object detection models using Faster R-CNN.

🔗 What You'll Learn:
1- How to annotate and prepare data for Faster R-CNN
2- Training on a custom dataset using a pre-trained backbone
3- Evaluating and testing the trained model on unseen data

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