Update README.md

README.md

@@ -1,26 +1,22 @@
 ---
 license: apache-2.0
-pinned: true
 ---
 
 # InsectSAM: Insect Segmentation and Monitoring
 
-<p align="left">
-  <a href="" rel="noopener">
- <img width=200px height=200px src="https://i.imgur.com/hjWgAN9.png alt="Project logo"></a>
-</p>
+![rb-ibdm-banner](https://github.com/martintmv-git/RB-IBDM/assets/101264514/a22f2069-e3c8-4a4f-a314-59cb01b39b66)
 
 ## Overview
 
-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.
+InsectSAM is a fine-tuned version of Meta AI's `segment-anything` model, optimized for insect segmentation and monitoring in the Netherlands. Designed for use with the [DIOPSIS](https://diopsis.eu) camera systems, algorithms and datasets, it enhances the accuracy of insect biodiversity segmentation from complex backgrounds.
 
 ## Purpose
 
-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.
+Trained to segment insects against diverse backgrounds, InsectSAM adapts to changing environments, ensuring its long-term utility for the DIOPSIS datasets.
 
 ## Model Architecture
 
-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.
+Built on the `segment-anything` architecture, InsectSAM is fine-tuned on an insect-specific dataset and integrated with GroundingDINO for improved detail recognition.
 
 ## Quick Start
 
@@ -33,12 +29,13 @@ InsectSAM utilizes the advanced capabilities of the `segment-anything` architect
 ### Usage
 
 #### Install 
-``` bash
+```bash
 !pip install --upgrade -q git+https://github.com/huggingface/transformers
 !pip install torch
 ```
-#### Load model directly via HF Transformers 🤗
-``` bash
+
+#### Load model via 🤗 Transformers
+```python
 from transformers import AutoProcessor, AutoModelForMaskGeneration
 
 processor = AutoProcessor.from_pretrained("martintmv/InsectSAM")
@@ -47,10 +44,9 @@ model = AutoModelForMaskGeneration.from_pretrained("martintmv/InsectSAM")
 
 ### Notebooks
 
-Three Jupyter notebooks are provided to demonstrate the model's capabilities and its integration with GroundingDINO:
-
-- **InsectSAM.ipynb**: Covers the training process, from data preparation to model evaluation.
-- **InsectSAM_GroundingDINO.ipynb**: Demonstrates how InsectSAM is combined with GroundingDINO for enhanced segmentation performance.
-- **Run_InsectSAM_Inference_Transformers.ipynb**: Run InsectSAM using Transformers.
+Explore InsectSAM's capabilities and integration with GroundingDINO through three Jupyter notebooks available on the RB-IBDM GitHub page:
+- [**InsectSAM.ipynb**](https://github.com/martintmv-git/RB-IBDM/blob/main/InsectSAM/InsectSAM.ipynb): Training process
+- [**InsectSAM_GroundingDINO.ipynb**](https://github.com/martintmv-git/RB-IBDM/blob/main/InsectSAM/InsectSAM_GroundingDINO.ipynb): Enhanced segmentation performance with GroundingDINO
+- [**InsectSAM_script.ipynb**](https://github.com/martintmv-git/RB-IBDM/tree/main/Image%20Processing%20Scripts/InsectSAM): Image processing script
 
-Check out the notebooks on RB-IBDM's GitHub page - https://github.com/martintmv-git/RB-IBDM/tree/main/InsectSAM
+GitHub: https://github.com/martintmv-git/RB-IBDM/tree/main/InsectSAM