README.md

metadata

title: Aegis-River-Segmentation
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 5.23.3
app_file: app.py
pinned: false
license: cc-by-nc-nd-4.0

Aegis-River-Segmentation

Water body segmentation from river/waterway images using SegFormer-B2. This Space loads the trained checkpoint from beaunix/river-segmentation and runs it on ZeroGPU.

How it works

1. The underlying model was trained as a 7-class semantic segmentation model (background, water, sky, vegetation, building, vehicle, person) on a combined RIWA + Parepare flood dataset.
2. In practice, only the water class is reliable; the other 6 classes are not used. This Space collapses the output to a binary mask: water vs. everything else (rendered as black).
3. The uploaded image is processed by SegformerImageProcessor (resize to 512x512, ImageNet-style normalization), run through the model, and the logits are upsampled back to the original image resolution before thresholding.
4. The report shows the original image, the binary water mask, an overlay, and the water coverage ratio.

Important: dependency pinning

This Space requires transformers==4.45.2 specifically. A later transformers release renamed an internal SegFormer decode_head submodule (decode_head.linear_projections -> decode_head.linear_c), which silently breaks the correspondence between this checkpoint's saved keys and the model architecture on a mismatched version. Do not upgrade transformers.

Notes

• Input is a single image (drag and drop), not a video.
• The 7-class architecture is preserved internally (required to load the checkpoint correctly), but only the water class is surfaced to the user.

License

CC BY-NC-ND 4.0 (Attribution - NonCommercial - NoDerivatives). See https://creativecommons.org/licenses/by-nc-nd/4.0/