README.md

metadata

title: FMODetect v2
emoji: 🌀
colorFrom: gray
colorTo: indigo
sdk: docker
app_port: 7860
pinned: false
license: mit
short_description: Fast-moving-object detection from a single blurred frame

FMODetect v2 — research demo

PyTorch re-implementation of FMODetect (Rozumnyi et al., ICCV 2021) with three additions: CBAM attention, a joint TDF + matting head, and an uncertainty-weighted boundary loss.

Source: https://github.com/jai-krishna-0921/FMODetect-v2

How this Space is built

This Space contains only a Dockerfile, a requirements.txt and this README. At build time the Dockerfile clones the source repo, builds the Next.js UI as a static export, and serves it from FastAPI on port 7860.

Environment

Set these in the Space settings → Variables and secrets:

key	value
FMODETECT_HF_REPO	<your-username>/fmodetect-v2
FMODETECT_HF_FILENAME	best.pt (default; only set to override)

The checkpoint is downloaded once at first request and cached on the Space disk. To swap models, upload a new file to the HF Hub model repo and restart the Space.

Hardware

CPU Basic (free) runs inference in 2–3 s per image pair. T4 small ($0.40/hr) brings it under 200 ms.