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title: "AnyTraverse Studio ๐"
emoji: "๐"
colorFrom: "slate"
colorTo: "blue"
sdk: "gradio"
sdk_version: "6.22.0"
app_file: "app.py"
pinned: false
python_version: "3.12"
short_description: "Live off-road traversability evaluation dashboard with Human-in-the-Loop (AnyTraverse)"
tags:
- computer-vision
- robotics
- segmentation
- vlm
---
# ๐ AnyTraverse Studio โ Live Evaluation & HITL Dashboard
A live Gradio dashboard for evaluating the **AnyTraverse** zero-shot off-road
traversability framework ([paper](https://arxiv.org/abs/2506.16826),
[PyPI](https://pypi.org/project/anytraverse/)).
## Workflow
1. **Upload a video** of an off-road scene.
2. Set the traversability preferences (ฯ), scene-similarity threshold,
ROI-uncertainty threshold, ROI bounds and the VLM inference **frame skip**.
3. Press **โถ๏ธ Go / Reset**.
4. Watch per frame:
- raw image + ROI box ยท traversability map ยท uncertainty map ยท ROI crop
- **attention maps for all prompts** (live, not in the exported video)
- live dual-metric **ROI traversability + uncertainty** line plot
- horizontal **0โ1 gauge bars** for the two ROI scores
- the traversal state (`ok` / `unknown_scene` / `unknown_object`)
5. The run **halts** whenever `traversal_state != OK`. Provide an operator
update like `mud: -0.7; gravel: 0.6` (or just `ok`) and press **Resume**.
Thresholds and ROI bounds can be edited **live** during a run โ they are
applied directly to the running pipeline.
## Notes
- The VLM weights (CLIPSeg + CLIP) download on first run and are cached.
- The composed analysis video is exported as an H.264 `.mp4` (bundled
`imageio-ffmpeg`, no system ffmpeg required).
- GPU is recommended; the space is configured for a **T4** accelerator.
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