https://github.com/ozturkvedat/auto_dataannotation_yolov8n
Automating data annotation for a larger object detection model by training a mini CNN model, using YOLOv8n from Ultralytics.
https://github.com/ozturkvedat/auto_dataannotation_yolov8n
data-annotation python yolov8
Last synced: 2 months ago
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Automating data annotation for a larger object detection model by training a mini CNN model, using YOLOv8n from Ultralytics.
- Host: GitHub
- URL: https://github.com/ozturkvedat/auto_dataannotation_yolov8n
- Owner: OzturkVedat
- Created: 2024-08-20T07:53:56.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-08-20T09:21:33.000Z (almost 2 years ago)
- Last Synced: 2025-10-20T10:58:19.347Z (8 months ago)
- Topics: data-annotation, python, yolov8
- Language: Jupyter Notebook
- Homepage:
- Size: 5.58 MB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Automating Data Annotation with YOLOv8n 🚀
## Overview
This 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.
## Features
- Implements YOLOv8n for high-accuracy object detection with a limited dataset.
- Applies data augmentation and non-max suppression for better performance.
- Automates data annotation for training of larger models.
- Configurable dataset setup with `data.yaml`.
- Includes a Jupyter Notebook for training and evaluation.
## Pre-requisites
### Hardware
- **CPU**: Multi-core processor recommended.
- **GPU**: NVIDIA GPU with CUDA support (e.g., GTX 1650 or better) for faster training.
- **RAM**: 8GB or more.
### Software
- **Python**: Version 3.7+.
### Dependencies
- **Python Packages**:
- `ultralytics` (YOLOv8)
- `opencv-python`
- `numpy`
- `matplotlib` (optional)
## Setting Up
1. Clone the repository:
```bash
git clone https://github.com/OzturkVedat/Auto_DataAnnotation_Yolov8n
cd Auto_DataAnnotation_Yolov8n
2. Configure:
Edit `data.yaml` for your dataset and class names.
4. Run:
Open the Jupyter Notebook file (*.ipynb) and execute the cells to train and evaluate the model as needed.