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/Faster-R-CNN-on-custom-dataset-Using-Pytorch 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 Don't forget to LIKE, COMMENT, and SUBSCRIBE if you find this video helpful! 🙌