update README with comprehensive requirements and dependencies

README.md

@@ -37,9 +37,16 @@ To run these notebooks, you'll need:
 - Pillow
 - NumPy
 - OpenCV-Python
-- Moondream model dependencies
 - Matplotlib
 - Scikit-image
+- Moondream (for Moondream models)
+- SAM (Segment Anything) and Grounding DINO (for segmentation tasks)
+- Hugging Face Accelerate (for optimized inference)
+- Requests
+- Json (standard library, but used extensively)
+- tqdm (for progress bars)
+- BitsAndBytes (for quantized models)
+- SentencePiece (for tokenization, if needed)
 
 ## Setup
 
@@ -47,9 +54,14 @@ To run these notebooks, you'll need:
 2. Install required dependencies:
 
 ```bash
-pip install torch torchvision
+pip install torch torchvision torchaudio
 pip install transformers pillow numpy opencv-python matplotlib scikit-image
-pip install moondream
+pip install moondream jupyter
+pip install accelerate bitsandbytes sentencepiece
+pip install supervision  # For visualization and annotations
+pip install segment-anything  # For SAM model
+pip install groundingdino-py  # For Grounding DINO
+pip install huggingface_hub
 ```
 
 3. Launch Jupyter:
@@ -81,10 +93,14 @@ Each notebook serves a specific purpose in the image annotation pipeline:
 ## Dependencies
 
 - [Moondream](https://github.com/vikhyat/moondream) - Efficient vision-language model
+- [Segment Anything (SAM)](https://segment-anything.com/) - Advanced segmentation model
+- [Grounding DINO](https://github.com/IDEA-Research/GroundingDINO) - Open-set object detection
 - PyTorch - Deep learning framework
 - OpenCV - Computer vision operations
 - COCO API - Annotation format handling
 - Transformers - Hugging Face library for model processing
+- Supervision - Utilities for computer vision workflows
+- Accelerate - Optimization library for PyTorch models
 
 ## Notes