# mhamilton723/FeatUp

Official code for "FeatUp: A Model-Agnostic Frameworkfor Features at Any Resolution" ICLR 2024

Repository: https://github.com/mhamilton723/FeatUp
Canonical: https://ross.abutalabs.com/products/featup
Language: Jupyter Notebook
License: MIT
License Family: permissive
Last push: 2024-06-28T23:45:46+00:00

## Health v2 (maintenance only)
Score: 16/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 65
- inputs: {"age_days": 923, "days_push": 796, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1654, forks 96 (observed 2026-08-28T04:05:17.248183+00:00)

## What it is
FeatUp is a model-agnostic framework that upsamples the spatial resolution of deep neural network features by 16-32x without changing their semantics. It provides pretrained upsamplers and implicit upsampler fitting for vision models, released as official code for an ICLR 2024 paper.

## Use cases
- upsample features from vision transformers to high resolution
- improve spatial resolution of backbone features for segmentation
- get high-resolution feature maps from DINO or other models
- boost dense prediction tasks with sharper features
- fit an implicit upsampler to a single image
- use pretrained feature upsamplers via torch hub

## When to choose
- you need high-resolution feature maps from a frozen vision backbone
- you're doing dense prediction like segmentation or depth estimation
- you want a drop-in feature upsampler for multiple model architectures

## When to avoid
- you need general image super-resolution rather than feature upsampling
- you can't run GPU inference
- you need a production-supported library with long-term maintenance

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, deep-learning, image-processing, computer-vision
- domain: machine-learning, computer-vision, deep-learning
- platform: python, cross-platform
- tags: feature-upsampling, vision-transformers, iclr-2024, pretrained-models, torch-hub, dense-prediction, research, gpu

## Member repositories
- mhamilton723/FeatUp (main) score 16

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:17.248183+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:44:56.009529+00:00, confidence not recorded.
  - readme: https://github.com/mhamilton723/FeatUp (fetched 2026-08-28T04:05:17.248183+00:00, sha 3dfaa8d834db)
- Data as of 2026-08-30T08:39:29.467469+00:00.