{"id":18792815,"url":"https://github.com/phenomsg/cv-based-waste-identifier","last_synced_at":"2025-09-02T05:32:25.597Z","repository":{"id":180789362,"uuid":"665707217","full_name":"PhenomSG/CV-based-Waste-Identifier","owner":"PhenomSG","description":"A computer vision-based waste identifier utilizes advanced image processing techniques and machine learning","archived":false,"fork":false,"pushed_at":"2023-10-13T14:00:52.000Z","size":22069,"stargazers_count":1,"open_issues_count":0,"forks_count":4,"subscribers_count":2,"default_branch":"main","last_synced_at":"2024-11-07T21:25:30.216Z","etag":null,"topics":["binary","binaryclassification","cnn","computer-vision","deep-learning","python3"],"latest_commit_sha":null,"homepage":"","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/PhenomSG.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":"2023-07-12T20:12:48.000Z","updated_at":"2024-08-29T22:46:50.000Z","dependencies_parsed_at":"2024-08-06T08:23:27.046Z","dependency_job_id":null,"html_url":"https://github.com/PhenomSG/CV-based-Waste-Identifier","commit_stats":null,"previous_names":["sahajg009/computer_vision_based_waste_identifier","sahajg009/cv-based-waste-identifier","phenomsg/cv-based-waste-identifier"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PhenomSG%2FCV-based-Waste-Identifier","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PhenomSG%2FCV-based-Waste-Identifier/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PhenomSG%2FCV-based-Waste-Identifier/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PhenomSG%2FCV-based-Waste-Identifier/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/PhenomSG","download_url":"https://codeload.github.com/PhenomSG/CV-based-Waste-Identifier/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":231752094,"owners_count":18421267,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","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":["binary","binaryclassification","cnn","computer-vision","deep-learning","python3"],"created_at":"2024-11-07T21:21:56.067Z","updated_at":"2024-12-29T15:25:42.015Z","avatar_url":"https://github.com/PhenomSG.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Computer Vision-based Waste Identifier 🌍♻️\n\nAn intelligent waste management system powered by computer vision to segregate recyclable and non-recyclable waste items.\n\n## Overview 📝\n\nThe Computer Vision-based Waste Identifier is a project aimed at revolutionizing waste management practices through cutting-edge technology. By utilizing advanced image processing and machine learning, this project tackles the challenge of accurately segregating recyclable and non-recyclable waste items.\n\n## Features 🌟\n\n- **Automated Segregation:** Our system employs computer vision algorithms to automatically identify and classify waste items in real-time.\n- **Recyclable vs. Non-recyclable:** It distinguishes between recyclable and non-recyclable waste, promoting efficient waste sorting.\n- **Accurate Classification:** Through deep learning techniques, the system achieves high accuracy in waste item categorization.\n- **User-Friendly Interface:** A user-friendly interface displays the segregation results and provides insights into waste management.\n\n## How It Works 🤖📸\n\n1. Cameras capture images of waste items.\n2. Computer vision algorithms process the images and extract relevant features.\n3. A trained model classifies the waste items as recyclable or non-recyclable.\n4. Results are presented through the user interface.\n\n## Future Prospects 🔮🌱\n\n- **Enhanced Recycling:** Accurate waste segregation boosts the quality of recycled materials, contributing to a more efficient recycling process.\n- **Environmental Impact:** Proper waste sorting reduces contamination and ensures proper disposal, minimizing environmental harm.\n- **Smart Waste Management:** Integration with IoT devices and data analytics could lead to optimized waste collection routes and schedules.\n- **Education and Awareness:** The system can be extended to raise awareness about waste classification and encourage responsible waste disposal.\n\n## Get Involved! 🚀\n\nContributions, feedback, and ideas are welcomed! Let's work together to create a cleaner, more sustainable future. 🌎♻️\n\n## Working\nA computer vision-based waste identifier utilizes advanced image processing techniques and machine learning algorithms to accurately classify and sort waste. Its key aspects include:\n\n**1. Image Capture:** Utilizing cameras or input devices to capture images of waste items.\n\n**2. Preprocessing:** Enhancing image quality, removing noise, and standardizing the dataset.\n\n**3. Feature Extraction:** Extracting relevant features from waste images for classification.\n\n**4. Classification Model:** Training machine learning models to identify and categorize different types of waste.\n\n**5. Real-time Identification:** Deploying the system to identify waste items in real-time, facilitating efficient waste management and recycling processes.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fphenomsg%2Fcv-based-waste-identifier","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fphenomsg%2Fcv-based-waste-identifier","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fphenomsg%2Fcv-based-waste-identifier/lists"}