73 lines
2.3 KiB
Markdown
73 lines
2.3 KiB
Markdown
# Image Annotation Project
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This repository contains Jupyter notebooks for image annotation using state-of-the-art vision-language models. The project focuses on image understanding, segmentation, and COCO format conversion.
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## Notebooks
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### 1. Image_Annotation_Testing_Satyam.ipynb
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This notebook provides testing capabilities for image annotation using advanced vision-language models. It includes various experiments to evaluate the performance and capabilities of the models in understanding and annotating images.
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### 2. Moondream_Segmentation_Satyam.ipynb
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This notebook implements segmentation capabilities using the Moondream vision-language model. It focuses on segmenting objects within images and generating precise boundaries for different objects in the scene.
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### 3. Moondream3_to_COCO_Satyam.ipynb
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This notebook handles the conversion of annotations to the COCO (Common Objects in Context) format. It takes segmented objects and converts them into a standardized JSON format suitable for training computer vision models.
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## Prerequisites
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To run these notebooks, you'll need:
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- Python 3.8+
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- Jupyter Notebook or JupyterLab
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- PyTorch
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- Transformers
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- Pillow
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- NumPy
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- OpenCV
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- Moondream model dependencies
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## Setup
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1. Clone or download this repository
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2. Install required dependencies:
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```bash
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pip install torch torchvision
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pip install transformers pillow numpy opencv-python
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```
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3. Launch Jupyter:
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```bash
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jupyter notebook
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```
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4. Open any of the notebooks and run the cells
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## Usage
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Each notebook can be run independently depending on your specific needs:
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1. Use `Image_Annotation_Testing_Satyam.ipynb` to test and evaluate image annotation capabilities
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2. Use `Moondream_Segmentation_Satyam.ipynb` for object segmentation tasks
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3. Use `Moondream3_to_COCO_Satyam.ipynb` to convert annotations to COCO format
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## Dependencies
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- [Moondream](https://github.com/vikhyat/moondream) - Vision-language model
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- PyTorch - Deep learning framework
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- OpenCV - Computer vision library
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- COCO API - For annotation format handling
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## Notes
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- Ensure you have sufficient GPU memory for running vision-language models
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- Models may require internet connectivity for initial downloads
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- Results may vary depending on the complexity of the images
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## Author
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Satyam - Image Annotation Project |