AutoAnotationModel

This repository contains the custom model checkpoint Model.pth and a powerful annotation pipeline developed by Rajkumar G for real-time object auto-annotation and YOLO dataset generation.

🔧 What This Is

AutoAnotationModel enables:

• ⚙️ Multi-class object detection using template-based region matching
• 🧠 Intelligent segmentation refinement using custom logic
• 🖼️ Live visual preview of annotations through a Flask web application
• 📤 YOLO-format .txt export for training detection models
• 📦 Exportable annotation packages including image + labels

The model and its web interface are used in applications like:

• Bin/Cover detection
• Mechanical part annotation
• Industrial dataset creation
• Real-time inspection and labeling

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🧠 Model Information

• Model Name: Model.pth
• Hosted on: Hugging Face - Rajkumar57/AutoAnotationModel
• Created by: Rajkumar G
• Frameworks Used: Python, PyTorch, OpenCV, Flask
• Use Case: Real-time annotation & YOLO label generation

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🚀 How to Use the Model

from huggingface_hub import hf_hub_download
from segment_anything import sam_model_registry, SamPredictor
import torch

# Download model from Hugging Face Hub
model_path = hf_hub_download(repo_id="Rajkumar57/AutoAnotationModel", filename="Model.pth")

# Load model
model = sam_model_registry["vit_h"](checkpoint=model_path)
model = model.to("cuda" if torch.cuda.is_available() else "cpu")
predictor = SamPredictor(model)

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🌟 Key Features of My Annotation System

• ✅ Web interface for user-defined class names and class IDs
• 📁 Upload template images for each class
• 🔍 Automatic region detection and segmentation
• 📤 YOLO-format .txt annotation generation
• 📸 Annotated image previews
• 📦 One-click export as .zip

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📁 Files Included

File	Description
Model.pth	Custom-trained model for annotation tasks
README.md	Documentation for usage

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👨‍💻 Author

Rajkumar G
📧 [email protected]
🔗 LinkedIn | GitHub

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📜 License

This model and pipeline were fully created by Rajkumar G and are released strictly for academic and research purposes only.

For commercial use, integration support, or contributions, feel free to reach out directly.

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Proudly developed and maintained by Rajkumar G. If you use this project, kindly give credit or cite this repository.