Update README.md

README.md

@@ -7,4 +7,274 @@ language:
 pipeline_tag: image-segmentation
 tags:
 - biology
----
+---
+
+# Kelp-RGB: Kelp Segmentation Model for RGB Drone Imagery
+
+**Model Type:** ONNX Semantic Segmentation  
+**Application:** Kelp forest detection in high-resolution RGB aerial imagery  
+**Input:** 3-band RGB imagery (Red, Green, Blue)  
+**Output:** Binary segmentation mask (kelp vs. non-kelp)
+
+## Model Description
+
+The Kelp-RGB model is a deep learning semantic segmentation model specifically trained for detecting kelp forests in RGB drone imagery. This model processes standard RGB imagery to provide accurate kelp segmentation for marine habitat monitoring and research, making it accessible for standard consumer drones and cameras.
+
+**Key Features:**
+- Optimized for standard RGB imagery from drones
+- ImageNet-pretrained normalization statistics
+- Efficient ONNX format for cross-platform deployment
+- Designed for high-resolution aerial photography (~3-7cm resolution)
+
+## Model Details
+
+- **Version:** 20250728
+- **Input Channels:** 3 (RGB)
+- **Input Size:** Dynamic tiling (recommended: 2048x2048 tiles)
+- **Normalization:** Standard (ImageNet statistics)
+- **Output:** Multi-class segmentation (0: background, 1: giant kelp, 2: bull kelp)
+- **Format:** ONNX
+
+### Normalization Parameters
+
+The model expects input images to be normalized using ImageNet statistics:
+
+```json
+{
+  "mean": [0.485, 0.456, 0.406],
+  "std": [0.229, 0.224, 0.225],
+  "max_pixel_value": 255.0
+}
+```
+
+## Usage
+
+### 1. Using kelp-o-matic CLI (recommended)
+
+For command-line usage:
+
+```bash
+# Install kelp-o-matic
+pip install kelp-o-matic
+# or
+conda install -c conda-forge kelp-o-matic
+
+# List available models
+kom list-models
+
+# Run kelp species segmentation on RGB drone imagery
+kom segment \
+    --model kelp-rgb \
+    --input /path/to/rgb_drone_image.tif \
+    --output /path/to/kelp_species_segmentation.tif \
+    --batch-size 8 \
+    --crop-size 2048 \
+    --blur-kernel 5 \
+    --morph-kernel 3
+
+# Use specific model version
+kom segment \
+    --model kelp-rgb \
+    --version 20250728 \
+    --input image.tif \
+    --output result.tif
+
+# For high-resolution imagery, use larger tiles
+kom segment \
+    --model kelp-rgb \
+    --input high_res_drone_image.tif \
+    --output result.tif \
+    --batch-size 4 \
+    --crop-size 1024
+```
+
+### 2. Using kelp-o-matic Python API
+
+The easiest way to use this model is through the kelp-o-matic package:
+
+```python
+from kelp_o_matic import model_registry
+
+# Load the model (automatically downloads if needed)
+model = model_registry["kelp-rgb"]
+
+# Process a large aerial image with automatic tiling
+model.process(
+    input_path="path/to/your/rgb_drone_image.tif",
+    output_path="path/to/output/kelp_species_segmentation.tif",
+    batch_size=8,  # Higher batch size for RGB
+    crop_size=2048,
+    blur_kernel_size=5,  # Post-processing median blur
+    morph_kernel_size=3,  # Morphological operations
+)
+
+# For more control, use the predict method directly
+import rasterio
+import numpy as np
+
+with rasterio.open("drone_image.tif") as src:
+    # Read a 2048x2048 tile (3 bands: RGB)
+    tile = src.read(window=((0, 2048), (0, 2048)))  # Shape: (3, 2048, 2048)
+    tile = np.transpose(tile, (1, 2, 0))  # Convert to HWC
+    
+    # Add batch dimension and predict
+    batch = np.expand_dims(tile, axis=0)  # Shape: (1, 2048, 2048, 3)
+    batch = np.transpose(batch, (0, 3, 1, 2))  # Convert to BCHW
+    
+    # Run inference (preprocessing handled automatically)
+    predictions = model.predict(batch)
+    
+    # Post-process to get final segmentation
+    segmentation = model.postprocess(predictions)
+    # Result: 0=background, 1=giant kelp, 2=bull kelp
+```
+
+### 3. Direct ONNX Runtime Usage
+
+```python
+import numpy as np
+import onnxruntime as ort
+from huggingface_hub import hf_hub_download
+from PIL import Image
+
+# Download the model
+model_path = hf_hub_download(repo_id="HakaiInstitute/kelp-rgb", filename="model.onnx")
+
+# Load the model
+session = ort.InferenceSession(model_path)
+
+# ImageNet normalization parameters
+mean = np.array([0.485, 0.456, 0.406])
+std = np.array([0.229, 0.224, 0.225])
+
+# Preprocess your RGB image
+def preprocess(image):
+    """
+    Preprocess RGB image for model input
+    image: numpy array of shape [height, width, 3] with pixel values 0-255
+    """
+    # Normalize to 0-1
+    image = image.astype(np.float32) / 255.0
+    
+    # Apply ImageNet normalization
+    image = (image - mean) / std
+    
+    # Reshape to model input format [batch, channels, height, width]
+    image = np.transpose(image, (2, 0, 1))  # HWC to CHW
+    image = np.expand_dims(image, axis=0)  # Add batch dimension
+    
+    return image
+
+# Load and preprocess image
+image = np.array(Image.open("drone_image.jpg"))
+preprocessed = preprocess(image)
+
+# Run inference
+input_name = session.get_inputs()[0].name
+output = session.run(None, {input_name: preprocessed})
+
+# Postprocess to get class predictions
+logits = output[0]  # Raw probabilities for each class
+prediction = np.argmax(logits, axis=1).squeeze(0).astype(np.uint8)
+# Result: 0=background, 1=giant kelp, 2=bull kelp
+```
+
+### 4. Using HuggingFace Hub Integration
+
+```python
+from huggingface_hub import hf_hub_download
+import onnxruntime as ort
+
+# Download and load model
+model_path = hf_hub_download(
+    repo_id="HakaiInstitute/kelp-rgb",
+    filename="model.onnx",
+    cache_dir="./models"
+)
+
+session = ort.InferenceSession(model_path)
+# ... continue with preprocessing and inference as above
+```
+
+## Installation
+
+### For kelp-o-matic usage:
+
+```bash
+# Via pip
+pip install kelp-o-matic
+
+# Via conda
+conda install -c conda-forge kelp-o-matic
+```
+
+### For direct ONNX usage:
+
+```bash
+pip install onnxruntime huggingface-hub numpy pillow
+# For GPU support:
+pip install onnxruntime-gpu
+```
+
+## Input Requirements
+
+- **Image Format:** 3-band RGB raster (JPEG, PNG, GeoTIFF)
+- **Band Order:** Red, Green, Blue
+- **Pixel Values:** Standard 8-bit (0-255 range) or 16-bit imagery
+- **Spatial Resolution:** Optimized for high-resolution drone imagery (cm-level)
+
+## Output Format
+
+- **Type:** Single-band raster with class labels
+- **Values:** 
+  - 0: Background (water, other features)
+  - 1: *Macrocystis pyrifera* (Giant kelp)
+  - 2: *Nereocystis luetkeana* (Bull kelp)
+- **Format:** Matches input raster format and projection
+- **Spatial Resolution:** Same as input
+
+**Note:** The model outputs class probabilities, but kelp-o-matic automatically applies argmax to convert these to discrete class labels.
+
+## Performance Notes
+
+- **Dynamic Tile Size:** Supports flexible tile sizes (recommended: 2048x2048 or 1024x1024)
+- **Batch Size:** Start with 4, adjust based on available GPU memory
+
+## Large Image Processing
+
+For processing large geospatial images, the kelp-o-matic package handles:
+
+- **Automatic Tiling:** Splits large images into manageable tiles
+- **Overlap Handling:** Uses overlapping tiles to avoid edge artifacts
+- **Memory Management:** Processes tiles in batches to manage memory usage
+- **Geospatial Metadata:** Preserves coordinate reference system and geotransforms
+- **Post-processing:** Optional median filtering and morphological operations
+
+## Citation
+
+If you use this model in your research, please cite:
+
+```bibtex
+@software{Denouden_Kelp-O-Matic,
+  author = {Denouden, Taylor and Reshitnyk, Luba},
+  doi = {10.5281/zenodo.7672166},
+  title = {{Kelp-O-Matic}},
+  url = {https://github.com/HakaiInstitute/kelp-o-matic}
+}
+```
+
+## License
+
+MIT License - see the [kelp-o-matic repository](https://github.com/HakaiInstitute/kelp-o-matic/blob/main/LICENSE) for details.
+
+## Related Resources
+
+- **Documentation:** [kelp-o-matic.readthedocs.io](https://kelp-o-matic.readthedocs.io)
+- **Source Code:** [github.com/HakaiInstitute/kelp-o-matic](https://github.com/HakaiInstitute/kelp-o-matic)
+- **Other Models:** Check the [Hakai Institute HuggingFace organization](https://huggingface.co/HakaiInstitute) for additional kelp segmentation models
+
+## Contact
+
+For questions or issues:
+- Open an issue on the [GitHub repository](https://github.com/HakaiInstitute/kelp-o-matic/issues)
+- Contact: [Hakai Institute](https://www.hakai.org)