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--- |
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license: apache-2.0 |
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pinned: true |
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--- |
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# InsectSAM: Insect Segmentation and Monitoring |
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<p align="left"> |
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<a href="" rel="noopener"> |
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<img width=200px height=200px src="https://i.imgur.com/hjWgAN9.png alt="Project logo"></a> |
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</p> |
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## Overview |
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InsectSAM is an advanced machine learning model tailored for the https://diopsis.eu camera systems and https://www.arise-biodiversity.nl/, dedicated to Insect Biodiversity Detection and Monitoring in the Netherlands. Built on Meta AI's `segment-anything` model, InsectSAM is fine-tuned to be accurate at segmenting insects from complex backgrounds, enhancing the accuracy and efficiency of biodiversity monitoring efforts. |
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## Purpose |
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This model has been meticulously trained to identify and segment insects against a variety of backgrounds that might otherwise confuse traditional algorithms. It is specifically designed to adapt to future changes in background environments, ensuring its long-term utility in the DIOPSIS / ARISE project. |
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## Model Architecture |
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InsectSAM utilizes the advanced capabilities of the `segment-anything` architecture, enhanced by our custom training on an insect-centric dataset. The model is further refined by integrating with GroundingDINO, improving its ability to distinguish fine details and subtle variations in insect appearances. |
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## Quick Start |
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### Prerequisites |
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- Python |
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- Hugging Face Transformers |
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- PyTorch |
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### Usage |
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#### Install |
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``` bash |
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!pip install --upgrade -q git+https://github.com/huggingface/transformers |
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!pip install torch |
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``` |
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#### Load model directly via HF Transformers 🤗 |
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``` bash |
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from transformers import AutoProcessor, AutoModelForMaskGeneration |
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processor = AutoProcessor.from_pretrained("martintmv/InsectSAM") |
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model = AutoModelForMaskGeneration.from_pretrained("martintmv/InsectSAM") |
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``` |
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### Notebooks |
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Three Jupyter notebooks are provided to demonstrate the model's capabilities and its integration with GroundingDINO: |
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- **InsectSAM.ipynb**: Covers the training process, from data preparation to model evaluation. |
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- **InsectSAM_GroundingDINO.ipynb**: Demonstrates how InsectSAM is combined with GroundingDINO for enhanced segmentation performance. |
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- **Run_InsectSAM_Inference_Transformers.ipynb**: Run InsectSAM using Transformers. |
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Check out the notebooks on RB-IBDM's GitHub page - https://github.com/martintmv-git/RB-IBDM/tree/main/InsectSAM |
