visaebench 0.1.0

A benchmark for evaluating Sparse Autoencoders (SAEs) on Vision Transformers

VISAEBench

VISAEBench is a benchmark for evaluating Sparse Autoencoders (SAEs) trained on Vision Transformer (ViT) patch activations. It scores an SAE across four interpretability dimensions (reconstruction, concept detection, spatial coherence, disentanglement) using 7 metrics (M1 through M7). It supports cross-backbone evaluation across CLIP, DINOv2, SigLIP, MAE, and DeiT (all ViT-B/16 or ViT-B/14).

Installation

# From PyPI (after v0.1.0 release)
pip install visaebench

# From source (development)
git clone https://github.com/vraj130/visaebench && cd visaebench
pip install -e .

Quickstart

import visaebench
from visaebench.hub import load_sae

sae = load_sae("visaebench/clip-vitb16-saes", subfolder="batchtopk_16x_k128")
results = visaebench.evaluate(
    sae=sae,
    backbone_name="clip_vitb16",
    imagenet_path="/path/to/imagenet/val",  # or None to stream from HF
)
for r in results:
    print(f"{r.name:30s} {r.value:.4f}")

Available SAE checkpoints

One HuggingFace repo per backbone; each holds 12 configs as subfolders (batchtopk_{8x,16x,32x}_k{64,128,192,256}).

Backbone	HuggingFace repo
CLIP ViT-B/16	visaebench/clip-vitb16-saes
DINOv2 ViT-B/14	visaebench/dinov2-vitb14-saes
SigLIP ViT-B/16	visaebench/siglip-vitb16-saes
MAE ViT-B/16	visaebench/mae-vitb16-saes
DeiT ViT-B/16	visaebench/deit-vitb16-saes

The detailed 12-config table (all batchtopk_{8x,16x,32x}_k{64,128,192,256} subfolders) lives in docs/quickstart.md.

Documentation

• docs/quickstart.md: end-to-end quickstart and the full checkpoint config table.
• docs/custom_models.md: evaluating your own SAE and custom backbones.
• docs/metrics.md: detailed description of the 7 metrics (M1 through M7).

Metrics

#	Registry key	Dimension	Description
M1	localization	spatial_coherence	Per-feature spatial coherence on patch grid
M2	fvu	reconstruction	Fraction of variance unexplained
M3	downstream_preservation	reconstruction	Linear probe accuracy after reconstruction vs raw
M4	sparse_probing	concept_detection	Sparse logistic probe AUC over class labels
M5	monosemanticity	concept_detection	Cross-model feature consistency
M6	cross_domain	concept_detection	Generalization to OOD datasets (EuroSAT, DTD)
M7	absorption	disentanglement	WordNet-hierarchy concept absorption rate

Citation

@inproceedings{visaebench2026,
  title     = {ViSAEBench: Cross-Backbone Evaluation of Vision Sparse Autoencoders Reveals Backbone-Dominated Variance and Metric Dissociations},
  author    = {TODO: maintainer to fill in author list},
  booktitle = {ICML 2026 Mechanistic Interpretability Workshop},
  year      = {2026}
}

License

Apache 2.0. See LICENSE.