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README.md

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+---
+tags:
+- object-detection
+- faster-rcnn
+- multispectral
+- canopy-detection
+- remote-sensing
+library_name: pytorch
+pipeline_tag: object-detection
+---
+
+# 🌳 Faster R-CNN 6-Band Canopy Detection
+
+**Faster R-CNN ResNet50-FPN v2** trained on 6-band multispectral satellite imagery for tree canopy detection.
+
+## Model Details
+
+| Property | Value |
+|----------|-------|
+| Backbone | ResNet50-FPN v2 (COCO pretrained) |
+| Input | 6 channels (B, G, R, RE, NIR1, NIR2) |
+| Classes | 1 (canopy) |
+| Image size | 640×640 |
+| Best mAP@50 | 0.9735 |
+| Parameters | ~43M |
+
+## Auto-Detect Band Support
+
+| Input Format | Band Mapping |
+|---|---|
+| 3-band (RGB) | [R, G, B, 0, 0, 0] |
+| 5-band | [B, G, R, RE, NIR1, 0] |
+| 6-band | Direct input |
+| 7-band | Drop Band 7 (anomalous NIR3) |
+
+## Usage
+
+```python
+import rasterio, numpy as np, torch
+from torchvision.models.detection import fasterrcnn_resnet50_fpn_v2
+
+# Load model (modify first conv for 6 channels)
+model = ...  # See training notebook for full setup
+model.load_state_dict(torch.load('best.pt'))
+model.eval()
+
+# Load & preprocess
+with rasterio.open('tile.tif') as src:
+    img = src.read()  # (C, H, W)
+# Map to 6 channels, normalize, run inference
+```