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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 excels at segmenting insects from complex backgrounds, enhancing the accuracy and efficiency of biodiversity monitoring efforts.
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  ## Purpose
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  ### Notebooks
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- Two 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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  Check out the notebooks on RB-IBDM's GitHub page - https://github.com/martintmv-git/RB-IBDM/tree/main/InsectSAM
 
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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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  ### 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