336labs/VisionEncoder-Eval-ReproData

A Strong Baseline for Evaluating Vision Encoders
in Multimodal Large Language Models

Yilin Yang^1,*  ·  Jun-Tao Tang^2,*  ·  Kengyi Wang³ ·  Siyuan Su³  ·  Gaoyong Luo⁴  ·  Mingda Chen^1,†

¹School of Artificial Intelligence, Shanghai Jiao Tong University
²Nanjing University  ·  ³Fudan University  ·  ⁴Independent Researcher
^*Equal contribution.   ^†Corresponding author.

Dataset Summary

Reproduction data, including precomputed vision and text features, for the evaluations in A Strong Baseline for Evaluating Vision Encoders in Multimodal Large Language Models. The release covers 70 vision encoders (43 language-supervised, 22 self-supervised, and 5 discrete), text features from three language models, and shared sample metadata.

Subsets

Subset	Samples	Contents	Size
lcs-1k	1,000	LCS-558K image patch features, text features, and captions	55.3 GiB
lcs-10k	10,000	LCS-558K image patch features, text features, and captions	553.0 GiB
imagenet-200k	200,000	ImageNet-1K pooled features, class labels, and kNN split	59.9 GiB

Each subset includes features for all 70 vision encoders. The LCS subsets also include text features from Qwen2.5-1.5B-Instruct, Qwen3-1.7B, and SmolLM2-1.7B-Instruct. The 10k arrays are stored in two 5k blocks per encoder/model.

Keep the shared sample row order when loading features. Shapes, extraction settings, and sampling details are recorded in the per-file metadata and sample manifests.

Encoder Catalog

encoders.json lists feature paths, shapes, metadata, and upstream encoder sources for all 70 encoders.

Tokenizer ID	Family	LCS [T, D]	ImageNet D	Feature files
toklip_l_384	DISCRETE	[576, 1152]	1152	patches · pooled · 10k blocks
toklip_s_256	DISCRETE	[256, 1152]	1152	patches · pooled · 10k blocks
uniar_bsq	DISCRETE	[1024, 4608]	4096	patches · pooled · 10k blocks
unitok_attn	DISCRETE	[256, 1024]	1024	patches · pooled · 10k blocks
vilau_256	DISCRETE	[256, 1024]	1024	patches · pooled · 10k blocks
clip_openai__l14	LANG	[256, 1024]	1024	patches · pooled · 10k blocks
mc1_b16_224_2.5b	LANG	[196, 768]	768	patches · pooled · 10k blocks
mc1_b16_224_400m	LANG	[196, 768]	768	patches · pooled · 10k blocks
mc1_b32_224_2.5b	LANG	[49, 768]	768	patches · pooled · 10k blocks
mc1_b32_224_400m	LANG	[49, 768]	768	patches · pooled · 10k blocks
mc1_g14_224_2.5b	LANG	[256, 1664]	1664	patches · pooled · 10k blocks
mc1_h14_224_2.5b	LANG	[256, 1280]	1280	patches · pooled · 10k blocks
mc1_h14_224_v1.2	LANG	[256, 1280]	1280	patches · pooled · 10k blocks
mc1_l14_224_2.5b	LANG	[256, 1024]	1024	patches · pooled · 10k blocks
mc1_l14_224_400m	LANG	[256, 1024]	1024	patches · pooled · 10k blocks
mc2_b16_224	LANG	[196, 768]	768	patches · pooled · 10k blocks
mc2_b16_384	LANG	[576, 768]	768	patches · pooled · 10k blocks
mc2_b32_224	LANG	[49, 768]	768	patches · pooled · 10k blocks
mc2_b32_224_mt5	LANG	[49, 768]	768	patches · pooled · 10k blocks
mc2_b32_384	LANG	[144, 768]	768	patches · pooled · 10k blocks
mc2_g14_224	LANG	[256, 1664]	1664	patches · pooled · 10k blocks
mc2_g14_378	LANG	[729, 1664]	1664	patches · pooled · 10k blocks
mc2_h14_378	LANG	[729, 1280]	1280	patches · pooled · 10k blocks
mc2_l14_224	LANG	[256, 1024]	1024	patches · pooled · 10k blocks
mc2_m16_224	LANG	[196, 512]	512	patches · pooled · 10k blocks
mc2_m16_224_mt5	LANG	[196, 512]	512	patches · pooled · 10k blocks
mc2_m16_384	LANG	[576, 512]	512	patches · pooled · 10k blocks
mc2_s16_224	LANG	[196, 384]	384	patches · pooled · 10k blocks
mc2_s16_224_mt5	LANG	[196, 384]	384	patches · pooled · 10k blocks
mc2_s16_384	LANG	[576, 384]	384	patches · pooled · 10k blocks
pe_core_b16_224	LANG	[196, 768]	768	patches · pooled · 10k blocks
pe_core_g14_448	LANG	[1024, 1536]	1536	patches · pooled · 10k blocks
pe_lang_l14_448	LANG	[1024, 1024]	1024	patches · pooled · 10k blocks
siglip2_b16_224	LANG	[196, 768]	768	patches · pooled · 10k blocks
siglip2_b16_256	LANG	[256, 768]	768	patches · pooled · 10k blocks
siglip2_b16_384	LANG	[576, 768]	768	patches · pooled · 10k blocks
siglip2_b16_512	LANG	[1024, 768]	768	patches · pooled · 10k blocks
siglip2_b32_256	LANG	[64, 768]	768	patches · pooled · 10k blocks
siglip2_g16_256	LANG	[256, 1536]	1536	patches · pooled · 10k blocks
siglip2_g16_384	LANG	[576, 1536]	1536	patches · pooled · 10k blocks
siglip2_l16_256	LANG	[256, 1024]	1024	patches · pooled · 10k blocks
siglip2_l16_384	LANG	[576, 1024]	1024	patches · pooled · 10k blocks
siglip2_l16_512	LANG	[1024, 1024]	1024	patches · pooled · 10k blocks
siglip2_sm14_224	LANG	[256, 1152]	1152	patches · pooled · 10k blocks
siglip2_sm14_384	LANG	[729, 1152]	1152	patches · pooled · 10k blocks
siglip2_sm16_256	LANG	[256, 1152]	1152	patches · pooled · 10k blocks
siglip2_sm16_384	LANG	[576, 1152]	1152	patches · pooled · 10k blocks
siglip2_sm16_512	LANG	[1024, 1152]	1152	patches · pooled · 10k blocks
dino_vitb16	SSL	[196, 768]	1536	patches · pooled · 10k blocks
dino_vitb8	SSL	[784, 768]	1536	patches · pooled · 10k blocks
dino_vits16	SSL	[196, 384]	768	patches · pooled · 10k blocks
dino_vits8	SSL	[784, 384]	768	patches · pooled · 10k blocks
dinov2_base	SSL	[256, 768]	1536	patches · pooled · 10k blocks
dinov2_giant	SSL	[256, 1536]	3072	patches · pooled · 10k blocks
dinov2_large	SSL	[256, 1024]	2048	patches · pooled · 10k blocks
dinov2_small	SSL	[256, 384]	768	patches · pooled · 10k blocks
dinov3_vitl16	SSL	[256, 1024]	2048	patches · pooled · 10k blocks
eupe_convnext_b	SSL	[64, 1024]	1024	patches · pooled · 10k blocks
eupe_vit_b	SSL	[256, 768]	1536	patches · pooled · 10k blocks
eupe_vit_s	SSL	[256, 384]	768	patches · pooled · 10k blocks
eupe_vit_t	SSL	[256, 192]	384	patches · pooled · 10k blocks
ijepa_vith14	SSL	[256, 1280]	1280	patches · pooled · 10k blocks
pixio_vitb16	SSL	[256, 768]	768	patches · pooled · 10k blocks
pixio_vith16	SSL	[256, 1280]	1280	patches · pooled · 10k blocks
pixio_vitl16	SSL	[256, 1024]	1024	patches · pooled · 10k blocks
raev2_dinov3l_k7	SSL	[256, 1024]	1024	patches · pooled · 10k blocks
webssl_dino1b_full2b_224	SSL	[256, 1536]	1536	patches · pooled · 10k blocks
webssl_mae1b_full2b_224	SSL	[256, 1536]	1536	patches · pooled · 10k blocks
webssl_mae300m_full2b_224	SSL	[196, 1024]	1024	patches · pooled · 10k blocks
webssl_mae3b_full2b_224	SSL	[256, 3072]	3072	patches · pooled · 10k blocks

Data Sources

This repository provides the three cached feature subsets listed below. Raw images, MLLM training data, evaluation benchmarks, and other baseline datasets are obtained separately.

Data used by each evaluation methods

Method	Data needed	Available here / source
RAVEL	Paired LCS-558K image patches and text features	lcs-1k/ and lcs-10k/
RSA, CCA, GW, MutualNN	Paired LCS-558K global image vectors and text features	Shared samples and text are included; prepare the required global image vectors separately
kNN	ImageNet training features, labels, and support/query split	imagenet-200k/
Linear probing	ImageNet train and validation images	ImageNet; the released 200k cache contains training images only
Alignment probing	COCO Karpathy captions; DreamLIP CC3M for the cross-dataset variants	COCO / Karpathy splits and DreamLIP captions
AC Policy / Law of Vision Representation	LCS-558K, Stage-1 projectors, and SPair-71k	LLaVA-Pretrain, Model Zoo, and SPair-71k
Mid-training loss	LCS-558K images/captions and projector-training logs	LLaVA-Pretrain; training instructions in the code repository
TokBench	Original images/annotations and matching tokenizer reconstructions	TokBench
MLLM training / evaluation	Training: LCS-558K and filtered mix665k; evaluation: the 11 benchmarks below	Model Zoo and the linked dataset sources

Downstream MLLM benchmarks used in this paper

Benchmark / required TSV	Source
MMMU_TEST.tsv	MMMU, test split
MMBench_TEST_EN_V11.tsv	MMBench, English test v1.1
VQAv2_VAL.tsv	VQA v2, validation split
ScienceQA_VAL.tsv	ScienceQA, validation split
ChartQA_TEST.tsv	ChartQA, test split
DocVQA_VAL.tsv	DocVQA, validation split
TextVQA_VAL.tsv	TextVQA, validation split
POPE.tsv	POPE
GQA_TestDev_Balanced.tsv	GQA, balanced test-dev split
MSCOCO_KARPATHY_TEST.tsv	Karpathy splits and COCO images
FLICKR30K_KARPATHY_TEST.tsv	Karpathy splits and Flickr30k images

Use the VLMEvalKit dataset loaders for benchmark preparation. Configuration used in our paper are in the code repository's Setup and Running sections.

Metadata and Integrity

The released arrays retain their original storage precision. Per-file metadata and sample manifests record available extraction and sampling information. FILES.json and checksums.sha256 provide file sizes and SHA-256 checksums.

Licensing and Attribution

See LICENSES.md. Use of the artifacts is subject to the applicable source-dataset and encoder terms. Source license information should be checked at the upstream links before reuse or redistribution; this card does not grant new rights over upstream images, captions, or model-derived artifacts.

Citation

If you use these feature caches or RAVEL, please cite the paper and the relevant source datasets and encoders:

@misc{yang2026strong,
  title={A Strong Baseline for Evaluating Vision Encoders in Multimodal Large Language Models},
  author={Yang, Yilin and Tang, Jun-Tao and Wang, Kengyi and Su, Siyuan and Luo, Gaoyong and Chen, Mingda},
  year={2026},
  eprint={2610.05413},
  archivePrefix={arXiv},
  primaryClass={cs.CV},
  url={https://arxiv.org/abs/2610.05413}
}