Mask R-CNN Model to detect the area of damage on a car. The rationale for such a model is that it can be used by insurance companies for faster processing of claims if users can upload pics and they can assess damage from them. This model can also be used by lenders if they are underwriting a car loan especially for a used car.

Code example demonstrating how to detect damage intensity of a damaged car using deep learning

A deep learning–based computer vision training pipeline for car damage detection using a Co-DETR learner enhanced with CBAM Attention, Hybrid Loss, and Albumentations. Trains on Colab to identify and localize car body defects such as scratches, dents, and rust. Includes end-to-end model training and quantitative evaluation.

Classifiying the damaged vehicles with deep learning models (keras and tensorflow).

Use publicly available data to train and classify damage incurred by cars, by OpenCV and CNN Networks

# Car Damage Detection using Detectron2 This project leverages **Detectron2**, a state-of-the-art object detection library, to detect car damages from images. The goal is to develop a model that can automatically identify and classify car damages, such as dents, scratches