{"id":26139854,"url":"https://github.com/ozturkvedat/auto_dataannotation_yolov8n","last_synced_at":"2026-04-18T11:32:11.107Z","repository":{"id":253911948,"uuid":"844910417","full_name":"OzturkVedat/Auto_DataAnnotation_Yolov8n","owner":"OzturkVedat","description":"Automating data annotation for a larger object detection model by training a mini CNN model, using YOLOv8n from Ultralytics.","archived":false,"fork":false,"pushed_at":"2024-08-20T09:21:33.000Z","size":5853,"stargazers_count":0,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-10-20T10:58:19.347Z","etag":null,"topics":["data-annotation","python","yolov8"],"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/OzturkVedat.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":"2024-08-20T07:53:56.000Z","updated_at":"2025-01-04T15:54:05.000Z","dependencies_parsed_at":"2024-08-20T10:10:04.254Z","dependency_job_id":null,"html_url":"https://github.com/OzturkVedat/Auto_DataAnnotation_Yolov8n","commit_stats":null,"previous_names":["ozturkvedat/auto_dataannotation_yolov8n"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/OzturkVedat/Auto_DataAnnotation_Yolov8n","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/OzturkVedat%2FAuto_DataAnnotation_Yolov8n","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/OzturkVedat%2FAuto_DataAnnotation_Yolov8n/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/OzturkVedat%2FAuto_DataAnnotation_Yolov8n/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/OzturkVedat%2FAuto_DataAnnotation_Yolov8n/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/OzturkVedat","download_url":"https://codeload.github.com/OzturkVedat/Auto_DataAnnotation_Yolov8n/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/OzturkVedat%2FAuto_DataAnnotation_Yolov8n/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31966950,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-18T00:39:45.007Z","status":"online","status_checked_at":"2026-04-18T02:00:07.018Z","response_time":103,"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":["data-annotation","python","yolov8"],"created_at":"2025-03-11T02:37:36.659Z","updated_at":"2026-04-18T11:32:11.080Z","avatar_url":"https://github.com/OzturkVedat.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Automating Data Annotation with YOLOv8n 🚀\n\n## Overview\n\nThis project aims to train a mini CNN model using a limited dataset (e.g. ~1k samples across 2 classes). The goal is to maximize detection efficiency so that the automated data annotation pipeline can accurately label new data, which will soon be fed into a larger CNN model.\n\n## Features\n\n- Implements YOLOv8n for high-accuracy object detection with a limited dataset.\n- Applies data augmentation and non-max suppression for better performance.\n- Automates data annotation for training of larger models.\n- Configurable dataset setup with `data.yaml`.\n- Includes a Jupyter Notebook for training and evaluation.\n\n## Pre-requisites\n\n### Hardware\n- **CPU**: Multi-core processor recommended.\n- **GPU**: NVIDIA GPU with CUDA support (e.g., GTX 1650 or better) for faster training.\n- **RAM**: 8GB or more.\n\n### Software\n- **Python**: Version 3.7+.\n  \n### Dependencies\n- **Python Packages**:\n  - `ultralytics` (YOLOv8)\n  - `opencv-python`\n  - `numpy`\n  - `matplotlib` (optional)\n \n## Setting Up\n\n1. Clone the repository:\n   ```bash\n   git clone https://github.com/OzturkVedat/Auto_DataAnnotation_Yolov8n\n   cd Auto_DataAnnotation_Yolov8n\n\n2. Configure:\n  Edit `data.yaml` for your dataset and class names.\n\n4. Run:\n  Open the Jupyter Notebook file (*.ipynb) and execute the cells to train and evaluate the model as needed.   \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fozturkvedat%2Fauto_dataannotation_yolov8n","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fozturkvedat%2Fauto_dataannotation_yolov8n","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fozturkvedat%2Fauto_dataannotation_yolov8n/lists"}