raflisbk/FETA-fasterrcnn-6band-canopy
By raflisbk · ↓ 0 · ♥ 0 · Updated 2026-02-10
What's in this repo
This page lists the files in raflisbk/FETA-fasterrcnn-6band-canopy, including fasterrcnn_6band_canopy.onnx.
- 1 ONNX models (.onnx files)
- 6 files, including 2 .pt, 1 .md, 1 .onnx, 1 .json
- Last updated 2026-02-10
ONNX files
| File | Size | Download |
|---|---|---|
| fasterrcnn_6band_canopy.onnx | — | Download |
How to use
These ONNX files can be loaded with ONNX Runtime. To inspect a model graph, upload the file to the Netron viewer. Download links point to the original files on Hugging Face.
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
Model info
object-detection
pytorch
pytorch
onnx
object-detection
faster-rcnn
multispectral
canopy-detection
remote-sensing
region:us
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♥ 0
Created: 2026-02-10
Updated: 2026-02-10
View on Hugging FaceFiles (6)
.gitattributes
README.md
best.pt
fasterrcnn_6band_canopy.onnx
ONNX
history.json
last.pt