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SigLIP2 NR IQA KonIQ

No-reference image-quality head on SigLIP2-SO400M for Apple Silicon (MLX). Independent from-paper reproduction of arXiv:2509.17374 — no upstream code/weights were released. The first MLX no-reference IQA head.

KindOtherBase modelgoogle/siglip2-so400m-patch16-512VersionRevision 8349820

Model source

Kind
Other
File size
5.17 MB
Base model
google/siglip2-so400m-patch16-512
Version
vRevision 8349820
Updated
Sep 28, 2026
Downloads
29
Source
Hugging Face

Source description

SigLIP2 NR-IQA (KonIQ-10k) — MLX

No-reference image-quality head on SigLIP2-SO400M for Apple Silicon (MLX). Independent from-paper reproduction of arXiv:2509.17374 — no upstream code/weights were released. The first MLX no-reference IQA head.

Ships the trained LoRA(q/k) + 3-layer MLP head adapter (~5 MB); the SigLIP2-SO400M backbone loads from the Google release at runtime.

Results (KonIQ-10k, 80/20 val)

SRCC 0.9575 · PLCC 0.9678 (paper reports SRCC ~0.932). Higher = better quality.

Usage

from siglip2_nriqa_mlx.score import NRIQAScorer
scorer = NRIQAScorer.from_pretrained("adapter.safetensors")  # backbone auto-loaded
print(scorer.score("photo.jpg"))   # ~0-100 MOS scale

Apache-2.0 (backbone Apache-2.0, Google). Training data KonIQ-10k is research-use — validate licensing for commercial deployment. See NOTICE.

Sources

1 source
Hugging FacePrimaryVersion 8349820ad6cfbc49183058cc124d75b25a167eeb
huggingface.co/mlx-community/SigLIP2-NR-IQA-KonIQ/tree/8349820ad6cfbc49183058cc124d75b25a167eeb

Verified Sep 28

Model artifacts

1 artifact

adapter.safetensors

safetensors · 5.17 MB · SHA-256 91342be7b2f8…f646 · Hugging Face

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Source excerpts

2 excerpts

--- license: apache-2.0 library_name: mlx pipeline_tag: image-classification tags: [mlx, image-quality-assessment, nr-iqa, siglip2, lora] base_model: google/siglip2-so400m-patch16-512 --- # SigLIP2 NR-IQA (KonIQ-10k) — MLX No-reference image-quality head on **SigLIP2-SO400M** for Apple Silicon (MLX). Independent from-paper reproduction of [arXiv:2509.17374](https://arxiv.org/abs/2509.17374) — no upstream code/weights were released. The first MLX no-reference IQA head. Ships the trained **LoRA(q/k) + 3-layer MLP head** adapter (~5 MB); the SigLIP2-SO400M backbone loads from the Google release at runtime. ## Results (KonIQ-10k, 80/20 val) **SRCC 0.9575 · PLCC 0.9678** (paper reports SRCC ~0.932). Higher = better quality. ## Usage ```python from siglip2_nriqa_mlx.score import NRIQAScorer scorer = NRIQAScorer.from_pretrained("adapter.safetensors") # backbone auto-loaded print(scorer.score("photo.jpg")) # ~0-100 MOS scale ``` Apache-2.0 (backbone Apache-2.0, Google). Training data KonIQ-10k is research-use — validate licensing for commercial deployment. See `NOTICE`.

Source context: 29 downloads · 1 likes · Pipeline image-classification · Library mlx · Repo mlx-community/SigLIP2-NR-IQA-KonIQ