Update README.md

README.md

@@ -276,15 +276,6 @@ python script_examples/basic_api_example.py
 python script_examples/websockets_api_example.py
 ```
 
-## Key Research Nodes
-
-| Node | Purpose | Location |
-|------|---------|----------|
-| FreSca | Frequency scaling | `_for_testing` category |
-| PAG | Attention perturbation | `model_patches/unet` |
-| SAG | Self-attention guidance | `model_patches` |
-| Mahiro | Directional guidance | `_for_testing` |
-
 ## Troubleshooting
 
 **CUDA Issues:**
@@ -358,7 +349,102 @@ After ComfyUI is set up:
 3. Configure the data paths in the workflow files
 4. Run the synthetic data generation pipeline
 
-## Troubleshooting
+# Data Directory
+
+This directory contains datasets and annotations for the SynSpill project.
+
+## Structure
+
+- `synthetic/` - Generated synthetic spill images and annotations
+- `real/` - Real-world industrial CCTV footage (test set)
+- `annotations/` - Ground truth labels and bounding boxes
+
+## Synthetic Data
+
+The synthetic dataset is generated using our AnomalInfusion pipeline:
+
+- Stable Diffusion XL for base image generation
+- IP adapters for style conditioning
+- Inpainting for precise spill placement
+
+## Citation
+
+If you use this data in your research, please cite our ICCV 2025 paper.
+=======
+# SynSpill Data Directory
+
+This directory contains datasets, annotations, and workflow configurations for the SynSpill project - a comprehensive dataset for industrial spill detection and synthesis.
+
+## Directory Structure
+
+```text
+data/
+├── README.md                    # This file
+├── generation_workflow.json    # ComfyUI workflow for synthetic image generation
+├── inpainting_workflow.json    # ComfyUI workflow for inpainting operations
+├── release/                     # Full dataset release
+│   ├── annotation_masks/        # Binary masks for spill regions (PNG format)
+│   ├── annotations/             # Ground truth annotations and metadata
+│   └── generated_images/        # Complete set of synthetic spill images
+└── samples/                     # Sample data for preview and testing
+    ├── annotation_masks/        # Sample binary masks
+    ├── generated_images/        # Sample synthetic images
+    └── inpainted_images/        # Sample inpainted results
+```
+
+## Dataset Contents
+
+### Release Dataset (`release/`)
+
+- **Generated Images**: High-quality synthetic industrial spill scenarios
+- **Annotation Masks**: Pixel-perfect binary masks identifying spill regions
+- **Annotations**: Structured metadata including bounding boxes, class labels, and scene descriptions
+
+### Sample Dataset (`samples/`)
+
+A subset of the full dataset for quick evaluation and testing purposes, containing:
+
+- Representative examples from each category
+- Various spill types and industrial environments
+- Both generated and inpainted image samples
+
+### Workflow Configurations
+
+- **`generation_workflow.json`**: ComfyUI workflow for generating base synthetic images using Stable Diffusion XL
+- **`inpainting_workflow.json`**: ComfyUI workflow for precise spill placement and inpainting operations
+
+## Synthetic Data Generation
+
+The synthetic dataset is created using our AnomalInfusion pipeline:
+
+1. **Base Generation**: Stable Diffusion XL creates industrial environment images
+2. **Style Conditioning**: IP adapters ensure consistent visual style across scenes
+3. **Spill Synthesis**: Controlled inpainting places realistic spills in specified locations
+4. **Mask Generation**: Automated creation of precise segmentation masks
+
+## Usage
+
+The data is organized for direct use with computer vision frameworks:
+
+- Images are in standard formats (PNG/JPG)
+- Masks are binary images (0 = background, 255 = spill)
+- Annotations follow standard object detection formats
+
+## Citation
+
+If you use this dataset in your research, please cite our ICCV 2025 paper:
+
+```bibtex
+@inproceedings{baranwal2025synspill,
+    title={SynSpill: Improved Industrial Spill Detection With Synthetic Data},
+    author={Baranwal, Aaditya and Bhatia, Guneet and Mueez, Abdul and Voelker, Jason and Vyas, Shruti},
+    booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision - Workshops (ICCV-W)},
+    year={2025}
+}
+```
+
+
+# Troubleshooting
 
 ### Common Issues
 
@@ -371,11 +457,3 @@ After ComfyUI is set up:
 - Use `--lowvram` flag if you have limited GPU memory
 - Consider using `--cpu` for CPU-only inference (slower)
 - Enable model offloading for better memory management
-
-## Next Steps
-
-Once ComfyUI is properly set up, you can proceed with:
-1. Loading the SynSpill workflows
-2. Configuring dataset paths
-3. Running synthetic data generation
-4. Training adaptation models