metadata
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
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