README.md

---
pretty_name: RAVEL Vision Encoder Features
language:
  - en
task_categories:
  - image-classification
  - other
license: other
license_name: upstream-data-and-model-terms
license_link: https://huggingface.co/datasets/336labs/VisionEncoder-Features/blob/main/LICENSES.md
size_categories:
  - 100K<n<1M
source_datasets:
  - liuhaotian/LLaVA-Pretrain
  - ILSVRC/imagenet-1k
tags:
  - vision-encoder-evaluation
  - multimodal
  - feature-extraction
  - ravel
  - numpy
  - arxiv:2610.05413
configs:
  - config_name: lcs558k_n1000_seed42
    default: true
    data_files:
      - split: train
        path: lcs558k/n1000_seed42/samples/samples.jsonl
  - config_name: lcs558k_n10000_seed42plus43
    data_files:
      - split: train
        path: lcs558k/n10000_seed42plus43/samples/samples.jsonl
  - config_name: imagenet1k_train200k
    data_files:
      - split: train
        path: imagenet1k/train200k/samples/samples.jsonl
---

<h1 align="center">A Strong Baseline for Evaluating Vision Encoders<br>in Multimodal Large Language Models</h1>

<p align="center"><strong>RAVEL · Vision Encoder Feature Dataset</strong></p>

<p align="center">Yilin Yang · Jun-Tao Tang · Kengyi Wang<br>Siyuan Su · Gaoyong Luo · Mingda Chen</p>

<p align="center">
  <a href="https://arxiv.org/abs/2610.05413"><img src="https://img.shields.io/badge/arXiv-2610.05413-b31b1b" alt="Paper"></a>
  <a href="https://github.com/JuntaoTang/MLLM-VisionEncoder-Eval"><img src="https://img.shields.io/badge/GitHub-Code-181717?logo=github" alt="Code"></a>
  <a href="https://huggingface.co/336labs/VisionEncoder-to-MLLM-ModelZoo"><img src="https://img.shields.io/badge/Model_Zoo-Checkpoints-FFD21E" alt="MLLM checkpoints"></a>
  <a href="https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main"><img src="https://img.shields.io/badge/Dataset-Features-blue" alt="Feature files"></a>
  <a href="#citation"><img src="https://img.shields.io/badge/Citation-BibTeX-blue" alt="Citation"></a>
</p>

## Dataset Summary

This dataset provides cached representations for the **70 vision encoders / visual tokenizers** evaluated in [A Strong Baseline for Evaluating Vision Encoders in Multimodal Large Language Models](https://arxiv.org/abs/2610.05413). It includes **43 language-supervised, 22 self-supervised, and 5 discrete** encoders, with shared sample order across encoders within each subset.

The release contains LCS-558K image patch features for RAVEL and other paired image–text probes, three matching language-model text feature arrays, and ImageNet-1K pooled image features for kNN and linear probes. Each encoder has its own metadata. The original NumPy arrays and storage precision are retained.

**Release status:** **LCS-10000 upload in progress.** LCS-1000 and ImageNet200k are complete. `FILES.json` lists the expected larger-cache contents; new arrays become available as batches are committed.

**Resources:** [Paper](https://arxiv.org/abs/2610.05413) · [Evaluation code](https://github.com/JuntaoTang/MLLM-VisionEncoder-Eval) · [MLLM Model Zoo and encoder weights](https://huggingface.co/336labs/VisionEncoder-to-MLLM-ModelZoo) · [Encoder catalog](encoders.json) · [File inventory](FILES.json)

## Dataset Structure

| Component | Arrays | Shape | Storage dtype | Size (GiB) |
| --- | ---: | --- | --- | ---: |
| LCS-1000 visual patches | 70 | `[1000, T, D]` | float16 | 55.27 |
| Aligned LLM text vectors | 3 | `[1000, D]` | float32 | 0.02 |
| LCS-10000 visual patches (two blocks per encoder) | 140 | `[5000, T, D]` | float16 | 552.74 |
| LCS-10000 historical last-token text (two blocks per model) | 6 | `[5000, D]` | float32 | 0.21 |
| ImageNet200k pooled features | 70 | `[200000, D]` | float32 | 59.89 |

Total prepared contents, including shared metadata and protocols: approximately **668.18 GiB**. NumPy sizes include their headers; no extra precision conversion was applied.

```text
README.md
LICENSES.md
encoders.json
FILES.json
checksums.sha256
lcs558k/n1000_seed42/
  samples/
    samples.jsonl
    manifest.json
    images_manifest.json
    source_indices.npy
  vision/
    <tokenizer_id>_patch_n1000_seed42.npy
    <tokenizer_id>_patch_n1000_seed42.json
  text/
    <llm_id>_penultimate_mean_n1000_seed42.npy
    <llm_id>_penultimate_mean_n1000_seed42.audit.json
lcs558k/n10000_seed42plus43/
  feature_manifest.json
  samples/{samples.jsonl,manifest.json,source_indices.npy}
  samples/part0_seed42/{samples.jsonl,manifest.json,source_indices.npy}
  samples/part1_seed43_disjoint/{samples.jsonl,manifest.json,source_indices.npy}
  samples/{disjoint_sample_audit.json,index_semantics.json}
  vision/<tokenizer_id>/{part0_seed42,part1_seed43_disjoint}.npy
  vision/<tokenizer_id>/{part0_seed42,part1_seed43_disjoint}.json
  text/<llm_id>/{part0_seed42,part1_seed43_disjoint}_penultimate_lasttok.npy
  text/<llm_id>/{part0_seed42,part1_seed43_disjoint}_penultimate_lasttok.audit.json
imagenet1k/train200k/
  samples/{samples.jsonl,manifest.json}
  features/<rank>_<tokenizer_id>.npy
  metadata/<rank>_<tokenizer_id>.json
  labels.npy
  source_indices.npy
  export_manifest.json
  manifest.tsv
  protocols/{knn_seed42.json,generation_receipt.json}
```

### LCS-558K: 1,000 paired examples, seed 42

The source is the `blip_laion_cc_sbu_558k.json` alignment annotation set from [LLaVA-Pretrain](https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain). The cached subset was sampled with `numpy.default_rng(42).choice`, without replacement, retaining the **unsorted sampled order**. The ordered manifest identifies the exact selected records; the original full-source annotation hash/revision is not recorded in every historical audit.

Each visual array has shape **`[1000, T, D]`**, stored as **float16**; patch count `T` and dimension `D` depend on the encoder. The text arrays are float32, with one vector per matching caption:

| ID | Language model | Shape | Text extraction |
| --- | --- | --- | --- |
| `qwen25` | [Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) | `[1000, 1536]` | Penultimate hidden state, mean over valid tokens |
| `qwen3` | [Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B) | `[1000, 2048]` | Penultimate hidden state, mean over valid tokens |
| `smollm2` | [SmolLM2-1.7B-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct) | `[1000, 2048]` | Penultimate hidden state, mean over valid tokens |

Text extraction uses layer `-2`, right padding, no truncation, bfloat16 inference, and float32 storage. The audits record pinned model revisions, file hashes, extraction settings, and software versions. These are the **mean-token** text features used by the current paper reproduction pipeline.

Row `i` in **every visual array and every text array** refers to row `i` of `samples/samples.jsonl`. Keep this order when comparing encoders. Fields are:

| Field | Meaning |
| --- | --- |
| `row_index` | Zero-based row in the released arrays |
| `source_index` | Record index in the original LCS alignment annotation list |
| `image_id` | Image path relative to the upstream image archive |
| `text_id` | Source text/example identifier |
| `text` | Caption used for text feature extraction |

The full manifest additionally retains the source conversation records and sampling information. Raw images are available from the upstream dataset.

### LCS-558K: 10,000 paired examples, seed 42 + disjoint seed 43

This larger cache contains **70 encoders × 10,000 images** as raw float16 patch tokens, stored in **two ordered 5,000-row blocks per encoder**. The first block uses the fixed seed-42 sample; the second uses seed 43 after excluding the first block by source annotation index, image ID, text ID, and image-file content hash. The original first block contains 11 repeated image-content rows; the disjoint-content check applies between blocks and within the new block. The two blocks have matching patch shapes and storage dtypes for each encoder.

| Block | Combined rows | Visual array shape | Sample order |
| --- | --- | --- | --- |
| `part0_seed42` | `0:5000` | `[5000, T, D]` | Fixed seed-42 sample, unsorted |
| `part1_seed43_disjoint` | `5000:10000` | `[5000, T, D]` | Disjoint seed-43 complement, unsorted |

The combined sample table has **10,000 records**. `row_index` addresses the combined sample table; `block_index` and `block_row_index` address the stored feature array. `source_index` identifies the original LCS annotation record. The released `source_indices.npy` files are reconstructed from the canonical manifests. Historical `indices_n5000` / `indices_new5000` files contained only `0..4999` block row positions; those positions are not original annotation indices.

The paired text cache contains **Qwen2.5-1.5B-Instruct, Qwen3-1.7B, and SmolLM2-1.7B-Instruct**, each as two float32 arrays of `[5000, D]` (`D=1536, 2048, 2048`). These historical arrays use the **penultimate hidden state at the last non-padding token**. Their metadata retains the original extraction audit and manifest hashes. Exact historical model revisions are absent from these audits. This text surface differs from the **mean over valid tokens** used by the published LCS-1000 paper cache; choose the intended surface explicitly.

Use [feature_manifest.json](lcs558k/n10000_seed42plus43/feature_manifest.json), the block sample manifests, and [index_semantics.json](lcs558k/n10000_seed42plus43/samples/index_semantics.json) when loading the arrays. The 1,000- and 5,000-sample seed-42 draws were sampled independently; **do not assume the LCS-1000 cache is the first 1,000 rows of this larger cache**. No visual pooling, PCA, whitening, or precision conversion was applied to these blocks.

### ImageNet-1K: 200,000 training examples

The subset contains **200 examples per class**, across **1,000 classes**, from the official ImageNet-1K training split. The export records a **support-sampling seed of 0**; the exact selected examples are given by `source_indices.npy`. All 70 arrays have shape **`[200000, D]`** and are stored as **float32**. Shared `source_indices.npy` is strictly increasing in the original train-index order; `labels.npy` contains integer class IDs `0–999`.

The sample table contains `row_index`, `source_index`, and `label`. Each array's readout is recorded in its metadata: for example, CLIP uses its final post-LayerNorm CLS before the visual projection, while the DINO readout concatenates normalized CLS and mean patch features. **Use the per-encoder representation description when interpreting dimensions.** These pooled/readout arrays have different semantics from the LCS patch-token arrays.

`protocols/knn_seed42.json` provides the existing deterministic **195-example training pool / 5-example query split per class**, with nested training subsets of **5, 10, 20, 45, 95, and 195** examples per class. Protocol indices address the **released 200,000-row arrays**; source indices identify examples in the original training split. The query split consists of held-out examples from that training subset. Full ImageNet training and validation features are outside this release.

## Download and Use

Install `huggingface_hub` and `numpy`; install `datasets` to load the sample tables.

### Download one encoder with aligned text features

```python
import json
from pathlib import Path
import numpy as np
from huggingface_hub import snapshot_download

root = Path(snapshot_download(
    repo_id="336labs/VisionEncoder-Features",
    repo_type="dataset",
    allow_patterns=[
        "encoders.json", "README.md", "LICENSES.md",
        "lcs558k/n1000_seed42/samples/**",
        "lcs558k/n1000_seed42/vision/clip_openai__l14*",
        "lcs558k/n1000_seed42/text/qwen3*",
    ],
    local_dir="VisionEncoder-Features",
))
base = root / "lcs558k/n1000_seed42"
vision = np.load(base / "vision/clip_openai__l14_patch_n1000_seed42.npy",
                 mmap_mode="r", allow_pickle=False)
text = np.load(base / "text/qwen3_penultimate_mean_n1000_seed42.npy",
               mmap_mode="r", allow_pickle=False)
samples = json.loads((base / "samples/manifest.json").read_text())["records"]
assert vision.shape[0] == text.shape[0] == len(samples) == 1000
```

### Download one encoder from the 10,000-sample cache

```python
import json
from pathlib import Path
import numpy as np
from huggingface_hub import snapshot_download

root = Path(snapshot_download(
    repo_id="336labs/VisionEncoder-Features",
    repo_type="dataset",
    allow_patterns=[
        "encoders.json", "README.md", "LICENSES.md",
        "lcs558k/n10000_seed42plus43/feature_manifest.json",
        "lcs558k/n10000_seed42plus43/samples/**",
        "lcs558k/n10000_seed42plus43/vision/clip_openai__l14/**",
        "lcs558k/n10000_seed42plus43/text/qwen3/**",
    ],
    local_dir="VisionEncoder-Features",
))
base = root / "lcs558k/n10000_seed42plus43"
blocks = ["part0_seed42", "part1_seed43_disjoint"]
vision_parts = [np.load(base / f"vision/clip_openai__l14/{part}.npy",
                        mmap_mode="r", allow_pickle=False) for part in blocks]
text_parts = [np.load(base / f"text/qwen3/{part}_penultimate_lasttok.npy",
                      mmap_mode="r", allow_pickle=False) for part in blocks]
samples = json.loads((base / "samples/manifest.json").read_text())["records"]
assert len(samples) == sum(x.shape[0] for x in vision_parts) == 10000
for block_index in range(2):
    assert vision_parts[block_index].shape[0] == text_parts[block_index].shape[0] == 5000
# A combined row r maps to vision_parts[r // 5000][r % 5000].
# Process the blocks in order to avoid allocating a full concatenated array.
```

### Download pooled ImageNet features

```python
import json
from pathlib import Path
import numpy as np
from huggingface_hub import snapshot_download

root = Path(snapshot_download(
    repo_id="336labs/VisionEncoder-Features",
    repo_type="dataset",
    allow_patterns=[
        "encoders.json", "README.md", "LICENSES.md",
        "imagenet1k/train200k/features/001_clip_openai__l14.npy",
        "imagenet1k/train200k/metadata/001_clip_openai__l14.json",
        "imagenet1k/train200k/labels.npy",
        "imagenet1k/train200k/source_indices.npy",
        "imagenet1k/train200k/samples/**",
        "imagenet1k/train200k/protocols/**",
    ],
    local_dir="VisionEncoder-Features",
))
base = root / "imagenet1k/train200k"
features = np.load(base / "features/001_clip_openai__l14.npy",
                   mmap_mode="r", allow_pickle=False)
labels = np.load(base / "labels.npy", allow_pickle=False)
protocol = json.loads((base / "protocols/knn_seed42.json").read_text())
train_rows = np.asarray(protocol["train_indices_by_shot"]["20"])
query_rows = np.asarray(protocol["query_indices"])
```

### Load the sample tables with Hugging Face Datasets

```python
from datasets import load_dataset

lcs_samples = load_dataset("336labs/VisionEncoder-Features",
                           "lcs558k_n1000_seed42", split="train")
lcs10k_samples = load_dataset("336labs/VisionEncoder-Features",
                              "lcs558k_n10000_seed42plus43", split="train")
imagenet_samples = load_dataset("336labs/VisionEncoder-Features",
                                "imagenet1k_train200k", split="train")
```

The three dataset configurations expose **sample metadata tables** in the Dataset Viewer and `load_dataset`. Download the large feature arrays separately and load them with NumPy. A complete download uses `snapshot_download` with `repo_type="dataset"` and no `allow_patterns`; selective downloads are usually sufficient.

## Encoder Catalog

`encoders.json` maps all 70 tokenizer IDs to their LCS-1000, LCS-10000 block, and ImageNet array paths, metadata, shapes, and upstream encoder sources. The `lcs_patch_n10000.blocks` entries specify each block's global row range and shared sample manifest. Upstream links identify the encoder projects and downloadable weights; an exact historical weight revision is provided only where the original audit recorded one.

| Tokenizer ID | Family | LCS `[T, D]` | ImageNet `D` | Feature files |
| --- | --- | --- | ---: | --- |
| [`toklip_l_384`](https://huggingface.co/TencentARC/TokLIP) | DISCRETE | `[576, 1152]` | 1152 | [patches](lcs558k/n1000_seed42/vision/toklip_l_384_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/011_toklip_l_384.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/toklip_l_384) |
| [`toklip_s_256`](https://huggingface.co/TencentARC/TokLIP) | DISCRETE | `[256, 1152]` | 1152 | [patches](lcs558k/n1000_seed42/vision/toklip_s_256_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/012_toklip_s_256.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/toklip_s_256) |
| [`uniar_bsq`](https://huggingface.co/ShareLab-SII/UniAR-SFT/tree/main/bsq_encoder) | DISCRETE | `[1024, 4608]` | 4096 | [patches](lcs558k/n1000_seed42/vision/uniar_bsq_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/013_uniar_bsq.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/uniar_bsq) |
| [`unitok_attn`](https://huggingface.co/FoundationVision/unitok_tokenizer) | DISCRETE | `[256, 1024]` | 1024 | [patches](lcs558k/n1000_seed42/vision/unitok_attn_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/014_unitok_attn.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/unitok_attn) |
| [`vilau_256`](https://huggingface.co/mit-han-lab/vila-u-7b-256) | DISCRETE | `[256, 1024]` | 1024 | [patches](lcs558k/n1000_seed42/vision/vilau_256_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/015_vilau_256.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/vilau_256) |
| [`clip_openai__l14`](https://huggingface.co/openai/clip-vit-large-patch14) | LANG | `[256, 1024]` | 1024 | [patches](lcs558k/n1000_seed42/vision/clip_openai__l14_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/001_clip_openai__l14.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/clip_openai__l14) |
| [`mc1_b16_224_2.5b`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[196, 768]` | 768 | [patches](lcs558k/n1000_seed42/vision/mc1_b16_224_2.5b_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/021_mc1_b16_224_2.5b.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/mc1_b16_224_2.5b) |
| [`mc1_b16_224_400m`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[196, 768]` | 768 | [patches](lcs558k/n1000_seed42/vision/mc1_b16_224_400m_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/022_mc1_b16_224_400m.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/mc1_b16_224_400m) |
| [`mc1_b32_224_2.5b`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[49, 768]` | 768 | [patches](lcs558k/n1000_seed42/vision/mc1_b32_224_2.5b_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/023_mc1_b32_224_2.5b.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/mc1_b32_224_2.5b) |
| [`mc1_b32_224_400m`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[49, 768]` | 768 | [patches](lcs558k/n1000_seed42/vision/mc1_b32_224_400m_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/024_mc1_b32_224_400m.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/mc1_b32_224_400m) |
| [`mc1_g14_224_2.5b`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[256, 1664]` | 1664 | [patches](lcs558k/n1000_seed42/vision/mc1_g14_224_2.5b_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/025_mc1_g14_224_2.5b.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/mc1_g14_224_2.5b) |
| [`mc1_h14_224_2.5b`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[256, 1280]` | 1280 | [patches](lcs558k/n1000_seed42/vision/mc1_h14_224_2.5b_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/026_mc1_h14_224_2.5b.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/mc1_h14_224_2.5b) |
| [`mc1_h14_224_v1.2`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[256, 1280]` | 1280 | [patches](lcs558k/n1000_seed42/vision/mc1_h14_224_v1.2_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/027_mc1_h14_224_v1.2.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/mc1_h14_224_v1.2) |
| [`mc1_l14_224_2.5b`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[256, 1024]` | 1024 | [patches](lcs558k/n1000_seed42/vision/mc1_l14_224_2.5b_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/028_mc1_l14_224_2.5b.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/mc1_l14_224_2.5b) |
| [`mc1_l14_224_400m`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[256, 1024]` | 1024 | [patches](lcs558k/n1000_seed42/vision/mc1_l14_224_400m_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/029_mc1_l14_224_400m.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/mc1_l14_224_400m) |
| [`mc2_b16_224`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[196, 768]` | 768 | [patches](lcs558k/n1000_seed42/vision/mc2_b16_224_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/030_mc2_b16_224.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/mc2_b16_224) |
| [`mc2_b16_384`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[576, 768]` | 768 | [patches](lcs558k/n1000_seed42/vision/mc2_b16_384_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/031_mc2_b16_384.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/mc2_b16_384) |
| [`mc2_b32_224`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[49, 768]` | 768 | [patches](lcs558k/n1000_seed42/vision/mc2_b32_224_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/032_mc2_b32_224.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/mc2_b32_224) |
| [`mc2_b32_224_mt5`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[49, 768]` | 768 | [patches](lcs558k/n1000_seed42/vision/mc2_b32_224_mt5_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/033_mc2_b32_224_mt5.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/mc2_b32_224_mt5) |
| [`mc2_b32_384`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[144, 768]` | 768 | [patches](lcs558k/n1000_seed42/vision/mc2_b32_384_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/034_mc2_b32_384.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/mc2_b32_384) |
| [`mc2_g14_224`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[256, 1664]` | 1664 | [patches](lcs558k/n1000_seed42/vision/mc2_g14_224_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/035_mc2_g14_224.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/mc2_g14_224) |
| [`mc2_g14_378`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[729, 1664]` | 1664 | [patches](lcs558k/n1000_seed42/vision/mc2_g14_378_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/036_mc2_g14_378.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/mc2_g14_378) |
| [`mc2_h14_378`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[729, 1280]` | 1280 | [patches](lcs558k/n1000_seed42/vision/mc2_h14_378_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/037_mc2_h14_378.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/mc2_h14_378) |
| [`mc2_l14_224`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[256, 1024]` | 1024 | [patches](lcs558k/n1000_seed42/vision/mc2_l14_224_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/038_mc2_l14_224.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/mc2_l14_224) |
| [`mc2_m16_224`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[196, 512]` | 512 | [patches](lcs558k/n1000_seed42/vision/mc2_m16_224_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/039_mc2_m16_224.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/mc2_m16_224) |
| [`mc2_m16_224_mt5`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[196, 512]` | 512 | [patches](lcs558k/n1000_seed42/vision/mc2_m16_224_mt5_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/040_mc2_m16_224_mt5.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/mc2_m16_224_mt5) |
| [`mc2_m16_384`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[576, 512]` | 512 | [patches](lcs558k/n1000_seed42/vision/mc2_m16_384_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/041_mc2_m16_384.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/mc2_m16_384) |
| [`mc2_s16_224`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[196, 384]` | 384 | [patches](lcs558k/n1000_seed42/vision/mc2_s16_224_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/042_mc2_s16_224.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/mc2_s16_224) |
| [`mc2_s16_224_mt5`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[196, 384]` | 384 | [patches](lcs558k/n1000_seed42/vision/mc2_s16_224_mt5_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/043_mc2_s16_224_mt5.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/mc2_s16_224_mt5) |
| [`mc2_s16_384`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[576, 384]` | 384 | [patches](lcs558k/n1000_seed42/vision/mc2_s16_384_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/044_mc2_s16_384.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/mc2_s16_384) |
| [`pe_core_b16_224`](https://huggingface.co/facebook/PE-Core-B16-224) | LANG | `[196, 768]` | 768 | [patches](lcs558k/n1000_seed42/vision/pe_core_b16_224_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/045_pe_core_b16_224.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/pe_core_b16_224) |
| [`pe_core_g14_448`](https://huggingface.co/facebook/PE-Core-G14-448) | LANG | `[1024, 1536]` | 1536 | [patches](lcs558k/n1000_seed42/vision/pe_core_g14_448_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/046_pe_core_g14_448.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/pe_core_g14_448) |
| [`pe_lang_l14_448`](https://huggingface.co/facebook/PE-Lang-L14-448) | LANG | `[1024, 1024]` | 1024 | [patches](lcs558k/n1000_seed42/vision/pe_lang_l14_448_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/047_pe_lang_l14_448.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/pe_lang_l14_448) |
| [`siglip2_b16_224`](https://github.com/google-research/big_vision/blob/main/big_vision/configs/proj/image_text/README_siglip2.md) | LANG | `[196, 768]` | 768 | [patches](lcs558k/n1000_seed42/vision/siglip2_b16_224_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/052_siglip2_b16_224.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/siglip2_b16_224) |
| [`siglip2_b16_256`](https://github.com/google-research/big_vision/blob/main/big_vision/configs/proj/image_text/README_siglip2.md) | LANG | `[256, 768]` | 768 | [patches](lcs558k/n1000_seed42/vision/siglip2_b16_256_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/053_siglip2_b16_256.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/siglip2_b16_256) |
| [`siglip2_b16_384`](https://github.com/google-research/big_vision/blob/main/big_vision/configs/proj/image_text/README_siglip2.md) | LANG | `[576, 768]` | 768 | [patches](lcs558k/n1000_seed42/vision/siglip2_b16_384_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/054_siglip2_b16_384.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/siglip2_b16_384) |
| [`siglip2_b16_512`](https://github.com/google-research/big_vision/blob/main/big_vision/configs/proj/image_text/README_siglip2.md) | LANG | `[1024, 768]` | 768 | [patches](lcs558k/n1000_seed42/vision/siglip2_b16_512_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/055_siglip2_b16_512.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/siglip2_b16_512) |
| [`siglip2_b32_256`](https://github.com/google-research/big_vision/blob/main/big_vision/configs/proj/image_text/README_siglip2.md) | LANG | `[64, 768]` | 768 | [patches](lcs558k/n1000_seed42/vision/siglip2_b32_256_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/056_siglip2_b32_256.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/siglip2_b32_256) |
| [`siglip2_g16_256`](https://github.com/google-research/big_vision/blob/main/big_vision/configs/proj/image_text/README_siglip2.md) | LANG | `[256, 1536]` | 1536 | [patches](lcs558k/n1000_seed42/vision/siglip2_g16_256_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/057_siglip2_g16_256.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/siglip2_g16_256) |
| [`siglip2_g16_384`](https://github.com/google-research/big_vision/blob/main/big_vision/configs/proj/image_text/README_siglip2.md) | LANG | `[576, 1536]` | 1536 | [patches](lcs558k/n1000_seed42/vision/siglip2_g16_384_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/058_siglip2_g16_384.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/siglip2_g16_384) |
| [`siglip2_l16_256`](https://github.com/google-research/big_vision/blob/main/big_vision/configs/proj/image_text/README_siglip2.md) | LANG | `[256, 1024]` | 1024 | [patches](lcs558k/n1000_seed42/vision/siglip2_l16_256_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/059_siglip2_l16_256.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/siglip2_l16_256) |
| [`siglip2_l16_384`](https://github.com/google-research/big_vision/blob/main/big_vision/configs/proj/image_text/README_siglip2.md) | LANG | `[576, 1024]` | 1024 | [patches](lcs558k/n1000_seed42/vision/siglip2_l16_384_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/060_siglip2_l16_384.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/siglip2_l16_384) |
| [`siglip2_l16_512`](https://github.com/google-research/big_vision/blob/main/big_vision/configs/proj/image_text/README_siglip2.md) | LANG | `[1024, 1024]` | 1024 | [patches](lcs558k/n1000_seed42/vision/siglip2_l16_512_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/061_siglip2_l16_512.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/siglip2_l16_512) |
| [`siglip2_sm14_224`](https://github.com/google-research/big_vision/blob/main/big_vision/configs/proj/image_text/README_siglip2.md) | LANG | `[256, 1152]` | 1152 | [patches](lcs558k/n1000_seed42/vision/siglip2_sm14_224_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/062_siglip2_sm14_224.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/siglip2_sm14_224) |
| [`siglip2_sm14_384`](https://github.com/google-research/big_vision/blob/main/big_vision/configs/proj/image_text/README_siglip2.md) | LANG | `[729, 1152]` | 1152 | [patches](lcs558k/n1000_seed42/vision/siglip2_sm14_384_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/063_siglip2_sm14_384.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/siglip2_sm14_384) |
| [`siglip2_sm16_256`](https://github.com/google-research/big_vision/blob/main/big_vision/configs/proj/image_text/README_siglip2.md) | LANG | `[256, 1152]` | 1152 | [patches](lcs558k/n1000_seed42/vision/siglip2_sm16_256_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/064_siglip2_sm16_256.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/siglip2_sm16_256) |
| [`siglip2_sm16_384`](https://github.com/google-research/big_vision/blob/main/big_vision/configs/proj/image_text/README_siglip2.md) | LANG | `[576, 1152]` | 1152 | [patches](lcs558k/n1000_seed42/vision/siglip2_sm16_384_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/065_siglip2_sm16_384.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/siglip2_sm16_384) |
| [`siglip2_sm16_512`](https://github.com/google-research/big_vision/blob/main/big_vision/configs/proj/image_text/README_siglip2.md) | LANG | `[1024, 1152]` | 1152 | [patches](lcs558k/n1000_seed42/vision/siglip2_sm16_512_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/066_siglip2_sm16_512.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/siglip2_sm16_512) |
| [`dino_vitb16`](https://huggingface.co/facebook/dino-vitb16) | SSL | `[196, 768]` | 1536 | [patches](lcs558k/n1000_seed42/vision/dino_vitb16_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/002_dino_vitb16.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/dino_vitb16) |
| [`dino_vitb8`](https://huggingface.co/facebook/dino-vitb8) | SSL | `[784, 768]` | 1536 | [patches](lcs558k/n1000_seed42/vision/dino_vitb8_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/003_dino_vitb8.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/dino_vitb8) |
| [`dino_vits16`](https://huggingface.co/facebook/dino-vits16) | SSL | `[196, 384]` | 768 | [patches](lcs558k/n1000_seed42/vision/dino_vits16_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/004_dino_vits16.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/dino_vits16) |
| [`dino_vits8`](https://huggingface.co/facebook/dino-vits8) | SSL | `[784, 384]` | 768 | [patches](lcs558k/n1000_seed42/vision/dino_vits8_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/005_dino_vits8.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/dino_vits8) |
| [`dinov2_base`](https://huggingface.co/facebook/dinov2-base) | SSL | `[256, 768]` | 1536 | [patches](lcs558k/n1000_seed42/vision/dinov2_base_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/006_dinov2_base.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/dinov2_base) |
| [`dinov2_giant`](https://huggingface.co/facebook/dinov2-giant) | SSL | `[256, 1536]` | 3072 | [patches](lcs558k/n1000_seed42/vision/dinov2_giant_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/007_dinov2_giant.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/dinov2_giant) |
| [`dinov2_large`](https://huggingface.co/facebook/dinov2-large) | SSL | `[256, 1024]` | 2048 | [patches](lcs558k/n1000_seed42/vision/dinov2_large_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/008_dinov2_large.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/dinov2_large) |
| [`dinov2_small`](https://huggingface.co/facebook/dinov2-small) | SSL | `[256, 384]` | 768 | [patches](lcs558k/n1000_seed42/vision/dinov2_small_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/009_dinov2_small.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/dinov2_small) |
| [`dinov3_vitl16`](https://huggingface.co/facebook/dinov3-vitl16-pretrain-lvd1689m) | SSL | `[256, 1024]` | 2048 | [patches](lcs558k/n1000_seed42/vision/dinov3_vitl16_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/010_dinov3_vitl16.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/dinov3_vitl16) |
| [`eupe_convnext_b`](https://huggingface.co/facebook/EUPE-ConvNeXt-B) | SSL | `[64, 1024]` | 1024 | [patches](lcs558k/n1000_seed42/vision/eupe_convnext_b_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/016_eupe_convnext_b.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/eupe_convnext_b) |
| [`eupe_vit_b`](https://huggingface.co/facebook/EUPE-ViT-B) | SSL | `[256, 768]` | 1536 | [patches](lcs558k/n1000_seed42/vision/eupe_vit_b_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/017_eupe_vit_b.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/eupe_vit_b) |
| [`eupe_vit_s`](https://huggingface.co/facebook/EUPE-ViT-S) | SSL | `[256, 384]` | 768 | [patches](lcs558k/n1000_seed42/vision/eupe_vit_s_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/018_eupe_vit_s.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/eupe_vit_s) |
| [`eupe_vit_t`](https://huggingface.co/facebook/EUPE-ViT-T) | SSL | `[256, 192]` | 384 | [patches](lcs558k/n1000_seed42/vision/eupe_vit_t_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/019_eupe_vit_t.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/eupe_vit_t) |
| [`ijepa_vith14`](https://github.com/facebookresearch/ijepa) | SSL | `[256, 1280]` | 1280 | [patches](lcs558k/n1000_seed42/vision/ijepa_vith14_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/020_ijepa_vith14.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/ijepa_vith14) |
| [`pixio_vitb16`](https://huggingface.co/facebook/pixio-vitb16) | SSL | `[256, 768]` | 768 | [patches](lcs558k/n1000_seed42/vision/pixio_vitb16_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/048_pixio_vitb16.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/pixio_vitb16) |
| [`pixio_vith16`](https://huggingface.co/facebook/pixio-vith16) | SSL | `[256, 1280]` | 1280 | [patches](lcs558k/n1000_seed42/vision/pixio_vith16_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/049_pixio_vith16.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/pixio_vith16) |
| [`pixio_vitl16`](https://huggingface.co/facebook/pixio-vitl16) | SSL | `[256, 1024]` | 1024 | [patches](lcs558k/n1000_seed42/vision/pixio_vitl16_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/050_pixio_vitl16.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/pixio_vitl16) |
| [`raev2_dinov3l_k7`](https://huggingface.co/nyu-visionx/RAEv2-models) | SSL | `[256, 1024]` | 1024 | [patches](lcs558k/n1000_seed42/vision/raev2_dinov3l_k7_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/051_raev2_dinov3l_k7.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/raev2_dinov3l_k7) |
| [`webssl_dino1b_full2b_224`](https://huggingface.co/facebook/webssl-dino1b-full2b-224) | SSL | `[256, 1536]` | 1536 | [patches](lcs558k/n1000_seed42/vision/webssl_dino1b_full2b_224_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/067_webssl_dino1b_full2b_224.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/webssl_dino1b_full2b_224) |
| [`webssl_mae1b_full2b_224`](https://huggingface.co/facebook/webssl-mae1b-full2b-224) | SSL | `[256, 1536]` | 1536 | [patches](lcs558k/n1000_seed42/vision/webssl_mae1b_full2b_224_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/068_webssl_mae1b_full2b_224.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/webssl_mae1b_full2b_224) |
| [`webssl_mae300m_full2b_224`](https://huggingface.co/facebook/webssl-mae300m-full2b-224) | SSL | `[196, 1024]` | 1024 | [patches](lcs558k/n1000_seed42/vision/webssl_mae300m_full2b_224_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/069_webssl_mae300m_full2b_224.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/webssl_mae300m_full2b_224) |
| [`webssl_mae3b_full2b_224`](https://huggingface.co/facebook/webssl-mae3b-full2b-224) | SSL | `[256, 3072]` | 3072 | [patches](lcs558k/n1000_seed42/vision/webssl_mae3b_full2b_224_patch_n1000_seed42.npy) · [pooled](imagenet1k/train200k/features/070_webssl_mae3b_full2b_224.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs558k/n10000_seed42plus43/vision/webssl_mae3b_full2b_224) |

## Dataset Creation and Provenance

This release assembles the project's existing feature caches. Feature arrays are published without additional pooling, quantization, or re-extraction. Released metadata uses repository-relative paths for shared samples and arrays. Inaccessible historical local paths are marked `source-local:<filename>`.

SHA-256 hashes of original metadata and original sample manifests are retained separately from the hashes of sanitized release files. For text features, `extraction_identity` preserves the original extraction identity; its manifest hash refers to the original manifest. The top-level `sample_manifest_sha256` refers to the sanitized published manifest, and `source_sample_manifest_sha256` retains the original hash.

All arrays passed finite-value checks before upload. Shapes/dtypes and row-order fingerprints were checked; the 70 encoder IDs match across both subsets. The ImageNet subset has exactly 200 examples per class; the kNN pool/query split is disjoint and shot subsets are nested. The repaired UniAR array matches its recorded repaired hash.

The added 140 patch arrays and six last-token text arrays passed full-file finite-value and SHA-256 checks before publication. All 70 encoder pairs have matching block shapes and dtypes. The component manifests concatenate exactly to the 10,000-row manifest, with no overlap in source index, image ID, or text ID. Text file hashes match their original extraction audits. Full source-index arrays are derived from the ordered canonical manifests, preserving the original caches.

The dataset inventory records array shapes, dtypes, byte sizes, SHA-256 hashes, and nonfinite-value counts. `checksums.sha256` verifies all listed files; it excludes itself and `FILES.json` to avoid circular hashes. Sources were preserved during publication.

## Intended Uses and Limitations

- **RAVEL and alignment probes:** use paired LCS visual and text features, preserving the shared row order. LCS-1000 uses mean-token text; the historical LCS-10000 cache uses last-token text. Keep these surfaces distinct. Captions and text embeddings are required for cross-modal probes.
- **kNN and linear probes:** use ImageNet pooled features with shared labels and the provided split. A full ImageNet linear-probe benchmark additionally requires the appropriate training and validation data.
- **Historical patch provenance:** the original visual audits do not record a uniform feature-layer specification. Missing extraction-layer, processor, or weight-revision fields remain explicitly unverified in this release. The arrays should be interpreted using available per-encoder metadata and the extraction code, rather than assuming a common layer/readout.
- **UniAR correction (LCS-1000 only):** the published LCS-1000 UniAR array uses the documented legacy BSQ deepstack concatenation. One NaN at `[630, 295, 1051]` was replaced by `1.7109375`, obtained by re-extraction with the official model. Its metadata retains original/repaired hashes, pinned model/source revisions, weight hash, and verification of the remaining 1,151 components in that BSQ vector.
- **Coverage and bias:** LCS-1000 and LCS-10000 are captioned-image samples; ImageNet200k is a class-balanced training subset. Findings depend on these data sources and selected encoders. Features inherit the content and representation biases of the upstream datasets and models.
- **Scope:** this release contains the three feature subsets described above. Obtain raw images and encoder weights from the upstream sources, and MLLM checkpoints from the Model Zoo. Derived PCA/distance caches and additional experiments beyond the documented subsets are outside the current release.

## Licensing and Attribution

See [LICENSES.md](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:

```bibtex
@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}
}
```

**Questions or corrections:** open a discussion on this dataset or an issue in the [evaluation code repository](https://github.com/JuntaoTang/MLLM-VisionEncoder-Eval).