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FETA-fasterrcnn-6band-canopy
raflisbk/FETA-fasterrcnn-6band-canopy
FETA-fasterrcnn-6band-canopy is a object detection model from raflisbk. Use it when you need objects located in an image. It is set up for pytorch.
Faster R-CNN ResNet50-FPN v2 trained on 6-band multispectral satellite imagery for tree canopy detection.
Access
Public
Updated Feb 10, 2026
Repo size
520 MB
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Repo makeup
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.pt347 MB · 67%
At a glance
- Task
- Object Detection
- Library
- pytorch
- Access
- Public
- Created
- Feb 10, 2026
- Updated
- Feb 10, 2026
- SHA
- a7627f3a
Model card
From the Hugging Face model README
🌳 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
- Task
- Object Detection
- Library
- pytorch
- Created
- Feb 10, 2026
- Updated
- Feb 10, 2026