{"id":24103540,"url":"https://github.com/rickydoan/deep-learning-car-damage-detection","last_synced_at":"2026-06-17T10:31:19.375Z","repository":{"id":271768179,"uuid":"914500409","full_name":"RickyDoan/Deep-Learning-Car-Damage-Detection","owner":"RickyDoan","description":null,"archived":false,"fork":false,"pushed_at":"2025-01-10T15:13:45.000Z","size":195444,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-28T06:27:28.976Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/RickyDoan.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-01-09T18:07:48.000Z","updated_at":"2025-01-10T15:13:48.000Z","dependencies_parsed_at":"2025-01-09T19:48:58.231Z","dependency_job_id":null,"html_url":"https://github.com/RickyDoan/Deep-Learning-Car-Damage-Detection","commit_stats":null,"previous_names":["rickydoan/deep-learning-car-damage-detection"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/RickyDoan/Deep-Learning-Car-Damage-Detection","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RickyDoan%2FDeep-Learning-Car-Damage-Detection","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RickyDoan%2FDeep-Learning-Car-Damage-Detection/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RickyDoan%2FDeep-Learning-Car-Damage-Detection/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RickyDoan%2FDeep-Learning-Car-Damage-Detection/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/RickyDoan","download_url":"https://codeload.github.com/RickyDoan/Deep-Learning-Car-Damage-Detection/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RickyDoan%2FDeep-Learning-Car-Damage-Detection/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":34445179,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-06-17T02:00:05.408Z","response_time":127,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2025-01-10T19:57:25.397Z","updated_at":"2026-06-17T10:31:19.360Z","avatar_url":"https://github.com/RickyDoan.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 🚗 Deep Learning - Car Damage Detection with CNNs\nThis repository contains an end-to-end pipeline for detecting and classifying car damages using Convolutional Neural Networks (CNNs) and pretrained models integrated with APIs and UI for deployment.\n![FF6CD62B-8F0C-4264-BD7F-C54ED7EEFD06_1_105_c](https://github.com/user-attachments/assets/2f535783-655b-4717-88d9-7561155aa68c)\n\n## Overview\nThis project aims to classify car damages into multiple categories like front crushed, rear breakages, normal conditions, etc., leveraging state-of-the-art deep learning technologies.\n### Features:\n- **Pretrained Models**: Transfer learning with **ResNet50**, **EfficientNet** with fine-tuning for task-specific accuracy.\n- **Custom CNN**: Built from scratch and compared with pretrained methods.\n- **Hyperparameter Tuning**: Automated using **Optuna**!\n- **FastAPI**: An API endpoint for real-time predictions.\n\n### Technical Highlights:\n- **Data Augmentation**: Extensive transformations for robust model generalization.\n- **GPU Training**: Utilized PyTorch for CUDA-accelerated training on google collab\n- **Inference Pipeline**: Image preprocessing, prediction, and visualization!\n\n### Usage:\n1. **Clone Repository**\n2. **Train from Scratch or Use Pretrained Weights**\n\n### **Evaluation**: \n* Confusion matrix and classification reports validate the models.\n* Accuracy is around 80%\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frickydoan%2Fdeep-learning-car-damage-detection","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frickydoan%2Fdeep-learning-car-damage-detection","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frickydoan%2Fdeep-learning-car-damage-detection/lists"}