Rename subsets and folders to lcs-1k, lcs-10k, and imagenet-200k
Browse filesUse matching subset and top-level folder names. Update README downloads, encoder catalog, metadata paths, LFS attributes, file inventory, and checksums. Preserve all NumPy feature objects and sample rows.
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- .gitattributes +1 -1
- FILES.json +0 -0
- README.md +105 -106
- checksums.sha256 +0 -0
- encoders.json +0 -0
- {imagenet1k/train200k → imagenet-200k}/export_manifest.json +1 -1
- {imagenet1k/train200k → imagenet-200k}/features/001_clip_openai__l14.npy +0 -0
- {imagenet1k/train200k → imagenet-200k}/features/002_dino_vitb16.npy +0 -0
- {imagenet1k/train200k → imagenet-200k}/features/003_dino_vitb8.npy +0 -0
- {imagenet1k/train200k → imagenet-200k}/features/004_dino_vits16.npy +0 -0
- {imagenet1k/train200k → imagenet-200k}/features/005_dino_vits8.npy +0 -0
- {imagenet1k/train200k → imagenet-200k}/features/006_dinov2_base.npy +0 -0
- {imagenet1k/train200k → imagenet-200k}/features/007_dinov2_giant.npy +0 -0
- {imagenet1k/train200k → imagenet-200k}/features/008_dinov2_large.npy +0 -0
- {imagenet1k/train200k → imagenet-200k}/features/009_dinov2_small.npy +0 -0
- {imagenet1k/train200k → imagenet-200k}/features/010_dinov3_vitl16.npy +0 -0
- {imagenet1k/train200k → imagenet-200k}/features/011_toklip_l_384.npy +0 -0
- {imagenet1k/train200k → imagenet-200k}/features/012_toklip_s_256.npy +0 -0
- {imagenet1k/train200k → imagenet-200k}/features/013_uniar_bsq.npy +0 -0
- {imagenet1k/train200k → imagenet-200k}/features/014_unitok_attn.npy +0 -0
- {imagenet1k/train200k → imagenet-200k}/features/015_vilau_256.npy +0 -0
- {imagenet1k/train200k → imagenet-200k}/features/016_eupe_convnext_b.npy +0 -0
- {imagenet1k/train200k → imagenet-200k}/features/017_eupe_vit_b.npy +0 -0
- {imagenet1k/train200k → imagenet-200k}/features/018_eupe_vit_s.npy +0 -0
- {imagenet1k/train200k → imagenet-200k}/features/019_eupe_vit_t.npy +0 -0
- {imagenet1k/train200k → imagenet-200k}/features/020_ijepa_vith14.npy +0 -0
- {imagenet1k/train200k → imagenet-200k}/features/021_mc1_b16_224_2.5b.npy +0 -0
- {imagenet1k/train200k → imagenet-200k}/features/022_mc1_b16_224_400m.npy +0 -0
- {imagenet1k/train200k → imagenet-200k}/features/023_mc1_b32_224_2.5b.npy +0 -0
- {imagenet1k/train200k → imagenet-200k}/features/024_mc1_b32_224_400m.npy +0 -0
- {imagenet1k/train200k → imagenet-200k}/features/025_mc1_g14_224_2.5b.npy +0 -0
- {imagenet1k/train200k → imagenet-200k}/features/026_mc1_h14_224_2.5b.npy +0 -0
- {imagenet1k/train200k → imagenet-200k}/features/027_mc1_h14_224_v1.2.npy +0 -0
- {imagenet1k/train200k → imagenet-200k}/features/028_mc1_l14_224_2.5b.npy +0 -0
- {imagenet1k/train200k → imagenet-200k}/features/029_mc1_l14_224_400m.npy +0 -0
- {imagenet1k/train200k → imagenet-200k}/features/030_mc2_b16_224.npy +0 -0
- {imagenet1k/train200k → imagenet-200k}/features/031_mc2_b16_384.npy +0 -0
- {imagenet1k/train200k → imagenet-200k}/features/032_mc2_b32_224.npy +0 -0
- {imagenet1k/train200k → imagenet-200k}/features/033_mc2_b32_224_mt5.npy +0 -0
- {imagenet1k/train200k → imagenet-200k}/features/034_mc2_b32_384.npy +0 -0
- {imagenet1k/train200k → imagenet-200k}/features/035_mc2_g14_224.npy +0 -0
- {imagenet1k/train200k → imagenet-200k}/features/036_mc2_g14_378.npy +0 -0
- {imagenet1k/train200k → imagenet-200k}/features/037_mc2_h14_378.npy +0 -0
- {imagenet1k/train200k → imagenet-200k}/features/038_mc2_l14_224.npy +0 -0
- {imagenet1k/train200k → imagenet-200k}/features/039_mc2_m16_224.npy +0 -0
- {imagenet1k/train200k → imagenet-200k}/features/040_mc2_m16_224_mt5.npy +0 -0
- {imagenet1k/train200k → imagenet-200k}/features/041_mc2_m16_384.npy +0 -0
- {imagenet1k/train200k → imagenet-200k}/features/042_mc2_s16_224.npy +0 -0
- {imagenet1k/train200k → imagenet-200k}/features/043_mc2_s16_224_mt5.npy +0 -0
- {imagenet1k/train200k → imagenet-200k}/features/044_mc2_s16_384.npy +0 -0
.gitattributes
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*.npy filter=lfs diff=lfs merge=lfs -text
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*.jsonl filter=lfs diff=lfs merge=lfs -text
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*.npy filter=lfs diff=lfs merge=lfs -text
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*.jsonl filter=lfs diff=lfs merge=lfs -text
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imagenet-200k/protocols/knn_seed42.json filter=lfs diff=lfs merge=lfs -text
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README.md
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- numpy
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- arxiv:2610.05413
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configs:
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- config_name:
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default: true
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data_files:
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- split: train
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path:
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data_files:
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path:
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data_files:
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path:
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---
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<h1 align="center">A Strong Baseline for Evaluating Vision Encoders<br>in Multimodal Large Language Models</h1>
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| Method | Data needed | Available here / source |
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|---|---|---|
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| RAVEL | Paired LCS-558K image patches and text features | `
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| 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 |
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| kNN | ImageNet training features, labels, and support/query split | `
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| Linear probing | ImageNet train and validation images | [ImageNet](https://www.image-net.org/download.php); the released 200k cache contains training images only |
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| Alignment probing | COCO Karpathy captions; DreamLIP CC3M for the cross-dataset variants | [COCO / Karpathy splits](https://cs.stanford.edu/people/karpathy/deepimagesent/) and [DreamLIP captions](https://huggingface.co/datasets/qidouxiong619/dreamlip_long_captions) |
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| AC Policy / Law of Vision Representation | LCS-558K, Stage-1 projectors, and SPair-71k | [LLaVA-Pretrain](https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain), [Model Zoo](https://huggingface.co/336labs/VisionEncoder-to-MLLM-ModelZoo), and [SPair-71k](https://cvlab.postech.ac.kr/research/SPair-71k/index.html) |
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```text
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features/
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imagenet1k/train200k/
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features/
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metadata/
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samples/
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checksums.sha256
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```
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### LCS-
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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.
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The full manifest additionally retains the source conversation records and sampling information. Raw images are available from the upstream dataset.
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### LCS-
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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.
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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.
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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.
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Use [feature_manifest.json](
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### ImageNet-
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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`.
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repo_type="dataset",
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allow_patterns=[
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"encoders.json", "README.md", "LICENSES.md",
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],
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local_dir="features",
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))
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base = root / "
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vision = np.load(base / "vision/clip_openai__l14_patch_n1000_seed42.npy",
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mmap_mode="r", allow_pickle=False)
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text = np.load(base / "text/qwen3_penultimate_mean_n1000_seed42.npy",
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repo_type="dataset",
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allow_patterns=[
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"encoders.json", "README.md", "LICENSES.md",
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local_dir="features",
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))
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base = root / "
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blocks = ["part0_seed42", "part1_seed43_disjoint"]
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vision_parts = [np.load(base / f"vision/clip_openai__l14/{part}.npy",
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mmap_mode="r", allow_pickle=False) for part in blocks]
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repo_type="dataset",
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allow_patterns=[
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"encoders.json", "README.md", "LICENSES.md",
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base = root / "
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features = np.load(base / "features/001_clip_openai__l14.npy",
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mmap_mode="r", allow_pickle=False)
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labels = np.load(base / "labels.npy", allow_pickle=False)
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from datasets import load_dataset
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lcs_samples = load_dataset("336labs/VisionEncoder-Features",
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lcs10k_samples = load_dataset("336labs/VisionEncoder-Features",
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imagenet_samples = load_dataset("336labs/VisionEncoder-Features",
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```
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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.
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| Tokenizer ID | Family | LCS `[T, D]` | ImageNet `D` | Feature files |
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| --- | --- | --- | ---: | --- |
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| [`toklip_l_384`](https://huggingface.co/TencentARC/TokLIP) | DISCRETE | `[576, 1152]` | 1152 | [patches](
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| [`toklip_s_256`](https://huggingface.co/TencentARC/TokLIP) | DISCRETE | `[256, 1152]` | 1152 | [patches](
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| [`uniar_bsq`](https://huggingface.co/ShareLab-SII/UniAR-SFT/tree/main/bsq_encoder) | DISCRETE | `[1024, 4608]` | 4096 | [patches](
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| [`unitok_attn`](https://huggingface.co/FoundationVision/unitok_tokenizer) | DISCRETE | `[256, 1024]` | 1024 | [patches](
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| [`vilau_256`](https://huggingface.co/mit-han-lab/vila-u-7b-256) | DISCRETE | `[256, 1024]` | 1024 | [patches](
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| [`clip_openai__l14`](https://huggingface.co/openai/clip-vit-large-patch14) | LANG | `[256, 1024]` | 1024 | [patches](
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| [`mc1_b16_224_2.5b`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[196, 768]` | 768 | [patches](
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| [`mc1_b16_224_400m`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[196, 768]` | 768 | [patches](
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| [`mc1_b32_224_2.5b`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[49, 768]` | 768 | [patches](
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| [`mc1_b32_224_400m`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[49, 768]` | 768 | [patches](
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| [`mc1_g14_224_2.5b`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[256, 1664]` | 1664 | [patches](
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| [`mc1_h14_224_2.5b`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[256, 1280]` | 1280 | [patches](
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| [`mc1_h14_224_v1.2`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[256, 1280]` | 1280 | [patches](
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| [`mc1_l14_224_2.5b`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[256, 1024]` | 1024 | [patches](
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| [`mc1_l14_224_400m`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[256, 1024]` | 1024 | [patches](
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| [`mc2_b16_224`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[196, 768]` | 768 | [patches](
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| [`mc2_b16_384`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[576, 768]` | 768 | [patches](
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| [`mc2_b32_224`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[49, 768]` | 768 | [patches](
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| [`mc2_b32_224_mt5`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[49, 768]` | 768 | [patches](
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| [`mc2_b32_384`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[144, 768]` | 768 | [patches](
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| [`mc2_g14_224`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[256, 1664]` | 1664 | [patches](
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| [`mc2_g14_378`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[729, 1664]` | 1664 | [patches](
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| [`mc2_h14_378`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[729, 1280]` | 1280 | [patches](
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| [`mc2_l14_224`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[256, 1024]` | 1024 | [patches](
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| [`mc2_m16_224`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[196, 512]` | 512 | [patches](
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| [`mc2_m16_224_mt5`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[196, 512]` | 512 | [patches](
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| [`mc2_m16_384`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[576, 512]` | 512 | [patches](
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| [`mc2_s16_224`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[196, 384]` | 384 | [patches](
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| [`mc2_s16_224_mt5`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[196, 384]` | 384 | [patches](
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| [`mc2_s16_384`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[576, 384]` | 384 | [patches](
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| [`pe_core_b16_224`](https://huggingface.co/facebook/PE-Core-B16-224) | LANG | `[196, 768]` | 768 | [patches](
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| [`pe_core_g14_448`](https://huggingface.co/facebook/PE-Core-G14-448) | LANG | `[1024, 1536]` | 1536 | [patches](
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| [`pe_lang_l14_448`](https://huggingface.co/facebook/PE-Lang-L14-448) | LANG | `[1024, 1024]` | 1024 | [patches](
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| [`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](
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| [`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](
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| [`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](
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| [`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](
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| [`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](
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| [`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](
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| [`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](
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| [`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](
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| 367 |
-
| [`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](
|
| 368 |
-
| [`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](
|
| 369 |
-
| [`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](
|
| 370 |
-
| [`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](
|
| 371 |
-
| [`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](
|
| 372 |
-
| [`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](
|
| 373 |
-
| [`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](
|
| 374 |
-
| [`dino_vitb16`](https://huggingface.co/facebook/dino-vitb16) | SSL | `[196, 768]` | 1536 | [patches](
|
| 375 |
-
| [`dino_vitb8`](https://huggingface.co/facebook/dino-vitb8) | SSL | `[784, 768]` | 1536 | [patches](
|
| 376 |
-
| [`dino_vits16`](https://huggingface.co/facebook/dino-vits16) | SSL | `[196, 384]` | 768 | [patches](
|
| 377 |
-
| [`dino_vits8`](https://huggingface.co/facebook/dino-vits8) | SSL | `[784, 384]` | 768 | [patches](
|
| 378 |
-
| [`dinov2_base`](https://huggingface.co/facebook/dinov2-base) | SSL | `[256, 768]` | 1536 | [patches](
|
| 379 |
-
| [`dinov2_giant`](https://huggingface.co/facebook/dinov2-giant) | SSL | `[256, 1536]` | 3072 | [patches](
|
| 380 |
-
| [`dinov2_large`](https://huggingface.co/facebook/dinov2-large) | SSL | `[256, 1024]` | 2048 | [patches](
|
| 381 |
-
| [`dinov2_small`](https://huggingface.co/facebook/dinov2-small) | SSL | `[256, 384]` | 768 | [patches](
|
| 382 |
-
| [`dinov3_vitl16`](https://huggingface.co/facebook/dinov3-vitl16-pretrain-lvd1689m) | SSL | `[256, 1024]` | 2048 | [patches](
|
| 383 |
-
| [`eupe_convnext_b`](https://huggingface.co/facebook/EUPE-ConvNeXt-B) | SSL | `[64, 1024]` | 1024 | [patches](
|
| 384 |
-
| [`eupe_vit_b`](https://huggingface.co/facebook/EUPE-ViT-B) | SSL | `[256, 768]` | 1536 | [patches](
|
| 385 |
-
| [`eupe_vit_s`](https://huggingface.co/facebook/EUPE-ViT-S) | SSL | `[256, 384]` | 768 | [patches](
|
| 386 |
-
| [`eupe_vit_t`](https://huggingface.co/facebook/EUPE-ViT-T) | SSL | `[256, 192]` | 384 | [patches](
|
| 387 |
-
| [`ijepa_vith14`](https://github.com/facebookresearch/ijepa) | SSL | `[256, 1280]` | 1280 | [patches](
|
| 388 |
-
| [`pixio_vitb16`](https://huggingface.co/facebook/pixio-vitb16) | SSL | `[256, 768]` | 768 | [patches](
|
| 389 |
-
| [`pixio_vith16`](https://huggingface.co/facebook/pixio-vith16) | SSL | `[256, 1280]` | 1280 | [patches](
|
| 390 |
-
| [`pixio_vitl16`](https://huggingface.co/facebook/pixio-vitl16) | SSL | `[256, 1024]` | 1024 | [patches](
|
| 391 |
-
| [`raev2_dinov3l_k7`](https://huggingface.co/nyu-visionx/RAEv2-models) | SSL | `[256, 1024]` | 1024 | [patches](
|
| 392 |
-
| [`webssl_dino1b_full2b_224`](https://huggingface.co/facebook/webssl-dino1b-full2b-224) | SSL | `[256, 1536]` | 1536 | [patches](
|
| 393 |
-
| [`webssl_mae1b_full2b_224`](https://huggingface.co/facebook/webssl-mae1b-full2b-224) | SSL | `[256, 1536]` | 1536 | [patches](
|
| 394 |
-
| [`webssl_mae300m_full2b_224`](https://huggingface.co/facebook/webssl-mae300m-full2b-224) | SSL | `[196, 1024]` | 1024 | [patches](
|
| 395 |
-
| [`webssl_mae3b_full2b_224`](https://huggingface.co/facebook/webssl-mae3b-full2b-224) | SSL | `[256, 3072]` | 3072 | [patches](
|
| 396 |
|
| 397 |
## Dataset Creation and Provenance
|
| 398 |
|
|
|
|
| 21 |
- numpy
|
| 22 |
- arxiv:2610.05413
|
| 23 |
configs:
|
| 24 |
+
- config_name: lcs-1k
|
| 25 |
default: true
|
| 26 |
data_files:
|
| 27 |
- split: train
|
| 28 |
+
path: lcs-1k/samples/samples.jsonl
|
| 29 |
+
- config_name: lcs-10k
|
| 30 |
data_files:
|
| 31 |
- split: train
|
| 32 |
+
path: lcs-10k/samples/samples.jsonl
|
| 33 |
+
- config_name: imagenet-200k
|
| 34 |
data_files:
|
| 35 |
- split: train
|
| 36 |
+
path: imagenet-200k/samples/samples.jsonl
|
| 37 |
---
|
| 38 |
|
| 39 |
<h1 align="center">A Strong Baseline for Evaluating Vision Encoders<br>in Multimodal Large Language Models</h1>
|
|
|
|
| 70 |
|
| 71 |
| Method | Data needed | Available here / source |
|
| 72 |
|---|---|---|
|
| 73 |
+
| RAVEL | Paired LCS-558K image patches and text features | `lcs-1k/` and `lcs-10k/` |
|
| 74 |
| 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 |
|
| 75 |
+
| kNN | ImageNet training features, labels, and support/query split | `imagenet-200k/` |
|
| 76 |
| Linear probing | ImageNet train and validation images | [ImageNet](https://www.image-net.org/download.php); the released 200k cache contains training images only |
|
| 77 |
| Alignment probing | COCO Karpathy captions; DreamLIP CC3M for the cross-dataset variants | [COCO / Karpathy splits](https://cs.stanford.edu/people/karpathy/deepimagesent/) and [DreamLIP captions](https://huggingface.co/datasets/qidouxiong619/dreamlip_long_captions) |
|
| 78 |
| AC Policy / Law of Vision Representation | LCS-558K, Stage-1 projectors, and SPair-71k | [LLaVA-Pretrain](https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain), [Model Zoo](https://huggingface.co/336labs/VisionEncoder-to-MLLM-ModelZoo), and [SPair-71k](https://cvlab.postech.ac.kr/research/SPair-71k/index.html) |
|
|
|
|
| 141 |
|
| 142 |
```text
|
| 143 |
features/
|
| 144 |
+
lcs-1k/{vision,text,samples}/
|
| 145 |
+
lcs-10k/{vision,text,samples}/
|
| 146 |
+
imagenet-200k/
|
|
|
|
| 147 |
features/
|
| 148 |
metadata/
|
| 149 |
samples/
|
|
|
|
| 155 |
checksums.sha256
|
| 156 |
```
|
| 157 |
|
| 158 |
+
### LCS-1k: 1,000 paired examples
|
| 159 |
|
| 160 |
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.
|
| 161 |
|
|
|
|
| 181 |
|
| 182 |
The full manifest additionally retains the source conversation records and sampling information. Raw images are available from the upstream dataset.
|
| 183 |
|
| 184 |
+
### LCS-10k: 10,000 paired examples
|
| 185 |
|
| 186 |
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.
|
| 187 |
|
|
|
|
| 192 |
|
| 193 |
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.
|
| 194 |
|
| 195 |
+
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.
|
| 196 |
|
| 197 |
+
Use [feature_manifest.json](lcs-10k/feature_manifest.json), the block sample manifests, and [index_semantics.json](lcs-10k/samples/index_semantics.json) when loading the arrays. No visual pooling, PCA, whitening, or precision conversion was applied to these blocks.
|
| 198 |
|
| 199 |
+
### ImageNet-200k: 200,000 training examples
|
| 200 |
|
| 201 |
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`.
|
| 202 |
|
|
|
|
| 221 |
repo_type="dataset",
|
| 222 |
allow_patterns=[
|
| 223 |
"encoders.json", "README.md", "LICENSES.md",
|
| 224 |
+
"lcs-1k/samples/**",
|
| 225 |
+
"lcs-1k/vision/clip_openai__l14*",
|
| 226 |
+
"lcs-1k/text/qwen3*",
|
| 227 |
],
|
| 228 |
local_dir="features",
|
| 229 |
))
|
| 230 |
+
base = root / "lcs-1k"
|
| 231 |
vision = np.load(base / "vision/clip_openai__l14_patch_n1000_seed42.npy",
|
| 232 |
mmap_mode="r", allow_pickle=False)
|
| 233 |
text = np.load(base / "text/qwen3_penultimate_mean_n1000_seed42.npy",
|
|
|
|
| 249 |
repo_type="dataset",
|
| 250 |
allow_patterns=[
|
| 251 |
"encoders.json", "README.md", "LICENSES.md",
|
| 252 |
+
"lcs-10k/feature_manifest.json",
|
| 253 |
+
"lcs-10k/samples/**",
|
| 254 |
+
"lcs-10k/vision/clip_openai__l14/**",
|
| 255 |
+
"lcs-10k/text/qwen3/**",
|
| 256 |
],
|
| 257 |
local_dir="features",
|
| 258 |
))
|
| 259 |
+
base = root / "lcs-10k"
|
| 260 |
blocks = ["part0_seed42", "part1_seed43_disjoint"]
|
| 261 |
vision_parts = [np.load(base / f"vision/clip_openai__l14/{part}.npy",
|
| 262 |
mmap_mode="r", allow_pickle=False) for part in blocks]
|
|
|
|
| 283 |
repo_type="dataset",
|
| 284 |
allow_patterns=[
|
| 285 |
"encoders.json", "README.md", "LICENSES.md",
|
| 286 |
+
"imagenet-200k/features/001_clip_openai__l14.npy",
|
| 287 |
+
"imagenet-200k/metadata/001_clip_openai__l14.json",
|
| 288 |
+
"imagenet-200k/labels.npy",
|
| 289 |
+
"imagenet-200k/source_indices.npy",
|
| 290 |
+
"imagenet-200k/samples/**",
|
| 291 |
+
"imagenet-200k/protocols/**",
|
| 292 |
],
|
| 293 |
local_dir="features",
|
| 294 |
))
|
| 295 |
+
base = root / "imagenet-200k"
|
| 296 |
features = np.load(base / "features/001_clip_openai__l14.npy",
|
| 297 |
mmap_mode="r", allow_pickle=False)
|
| 298 |
labels = np.load(base / "labels.npy", allow_pickle=False)
|
|
|
|
| 307 |
from datasets import load_dataset
|
| 308 |
|
| 309 |
lcs_samples = load_dataset("336labs/VisionEncoder-Features",
|
| 310 |
+
"lcs-1k", split="train")
|
| 311 |
lcs10k_samples = load_dataset("336labs/VisionEncoder-Features",
|
| 312 |
+
"lcs-10k", split="train")
|
| 313 |
imagenet_samples = load_dataset("336labs/VisionEncoder-Features",
|
| 314 |
+
"imagenet-200k", split="train")
|
| 315 |
```
|
| 316 |
|
| 317 |
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.
|
|
|
|
| 322 |
|
| 323 |
| Tokenizer ID | Family | LCS `[T, D]` | ImageNet `D` | Feature files |
|
| 324 |
| --- | --- | --- | ---: | --- |
|
| 325 |
+
| [`toklip_l_384`](https://huggingface.co/TencentARC/TokLIP) | DISCRETE | `[576, 1152]` | 1152 | [patches](lcs-1k/vision/toklip_l_384_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/011_toklip_l_384.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/toklip_l_384) |
|
| 326 |
+
| [`toklip_s_256`](https://huggingface.co/TencentARC/TokLIP) | DISCRETE | `[256, 1152]` | 1152 | [patches](lcs-1k/vision/toklip_s_256_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/012_toklip_s_256.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/toklip_s_256) |
|
| 327 |
+
| [`uniar_bsq`](https://huggingface.co/ShareLab-SII/UniAR-SFT/tree/main/bsq_encoder) | DISCRETE | `[1024, 4608]` | 4096 | [patches](lcs-1k/vision/uniar_bsq_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/013_uniar_bsq.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/uniar_bsq) |
|
| 328 |
+
| [`unitok_attn`](https://huggingface.co/FoundationVision/unitok_tokenizer) | DISCRETE | `[256, 1024]` | 1024 | [patches](lcs-1k/vision/unitok_attn_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/014_unitok_attn.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/unitok_attn) |
|
| 329 |
+
| [`vilau_256`](https://huggingface.co/mit-han-lab/vila-u-7b-256) | DISCRETE | `[256, 1024]` | 1024 | [patches](lcs-1k/vision/vilau_256_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/015_vilau_256.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/vilau_256) |
|
| 330 |
+
| [`clip_openai__l14`](https://huggingface.co/openai/clip-vit-large-patch14) | LANG | `[256, 1024]` | 1024 | [patches](lcs-1k/vision/clip_openai__l14_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/001_clip_openai__l14.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/clip_openai__l14) |
|
| 331 |
+
| [`mc1_b16_224_2.5b`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[196, 768]` | 768 | [patches](lcs-1k/vision/mc1_b16_224_2.5b_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/021_mc1_b16_224_2.5b.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/mc1_b16_224_2.5b) |
|
| 332 |
+
| [`mc1_b16_224_400m`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[196, 768]` | 768 | [patches](lcs-1k/vision/mc1_b16_224_400m_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/022_mc1_b16_224_400m.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/mc1_b16_224_400m) |
|
| 333 |
+
| [`mc1_b32_224_2.5b`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[49, 768]` | 768 | [patches](lcs-1k/vision/mc1_b32_224_2.5b_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/023_mc1_b32_224_2.5b.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/mc1_b32_224_2.5b) |
|
| 334 |
+
| [`mc1_b32_224_400m`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[49, 768]` | 768 | [patches](lcs-1k/vision/mc1_b32_224_400m_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/024_mc1_b32_224_400m.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/mc1_b32_224_400m) |
|
| 335 |
+
| [`mc1_g14_224_2.5b`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[256, 1664]` | 1664 | [patches](lcs-1k/vision/mc1_g14_224_2.5b_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/025_mc1_g14_224_2.5b.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/mc1_g14_224_2.5b) |
|
| 336 |
+
| [`mc1_h14_224_2.5b`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[256, 1280]` | 1280 | [patches](lcs-1k/vision/mc1_h14_224_2.5b_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/026_mc1_h14_224_2.5b.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/mc1_h14_224_2.5b) |
|
| 337 |
+
| [`mc1_h14_224_v1.2`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[256, 1280]` | 1280 | [patches](lcs-1k/vision/mc1_h14_224_v1.2_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/027_mc1_h14_224_v1.2.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/mc1_h14_224_v1.2) |
|
| 338 |
+
| [`mc1_l14_224_2.5b`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[256, 1024]` | 1024 | [patches](lcs-1k/vision/mc1_l14_224_2.5b_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/028_mc1_l14_224_2.5b.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/mc1_l14_224_2.5b) |
|
| 339 |
+
| [`mc1_l14_224_400m`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[256, 1024]` | 1024 | [patches](lcs-1k/vision/mc1_l14_224_400m_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/029_mc1_l14_224_400m.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/mc1_l14_224_400m) |
|
| 340 |
+
| [`mc2_b16_224`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[196, 768]` | 768 | [patches](lcs-1k/vision/mc2_b16_224_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/030_mc2_b16_224.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/mc2_b16_224) |
|
| 341 |
+
| [`mc2_b16_384`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[576, 768]` | 768 | [patches](lcs-1k/vision/mc2_b16_384_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/031_mc2_b16_384.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/mc2_b16_384) |
|
| 342 |
+
| [`mc2_b32_224`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[49, 768]` | 768 | [patches](lcs-1k/vision/mc2_b32_224_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/032_mc2_b32_224.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/mc2_b32_224) |
|
| 343 |
+
| [`mc2_b32_224_mt5`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[49, 768]` | 768 | [patches](lcs-1k/vision/mc2_b32_224_mt5_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/033_mc2_b32_224_mt5.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/mc2_b32_224_mt5) |
|
| 344 |
+
| [`mc2_b32_384`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[144, 768]` | 768 | [patches](lcs-1k/vision/mc2_b32_384_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/034_mc2_b32_384.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/mc2_b32_384) |
|
| 345 |
+
| [`mc2_g14_224`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[256, 1664]` | 1664 | [patches](lcs-1k/vision/mc2_g14_224_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/035_mc2_g14_224.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/mc2_g14_224) |
|
| 346 |
+
| [`mc2_g14_378`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[729, 1664]` | 1664 | [patches](lcs-1k/vision/mc2_g14_378_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/036_mc2_g14_378.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/mc2_g14_378) |
|
| 347 |
+
| [`mc2_h14_378`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[729, 1280]` | 1280 | [patches](lcs-1k/vision/mc2_h14_378_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/037_mc2_h14_378.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/mc2_h14_378) |
|
| 348 |
+
| [`mc2_l14_224`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[256, 1024]` | 1024 | [patches](lcs-1k/vision/mc2_l14_224_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/038_mc2_l14_224.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/mc2_l14_224) |
|
| 349 |
+
| [`mc2_m16_224`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[196, 512]` | 512 | [patches](lcs-1k/vision/mc2_m16_224_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/039_mc2_m16_224.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/mc2_m16_224) |
|
| 350 |
+
| [`mc2_m16_224_mt5`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[196, 512]` | 512 | [patches](lcs-1k/vision/mc2_m16_224_mt5_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/040_mc2_m16_224_mt5.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/mc2_m16_224_mt5) |
|
| 351 |
+
| [`mc2_m16_384`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[576, 512]` | 512 | [patches](lcs-1k/vision/mc2_m16_384_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/041_mc2_m16_384.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/mc2_m16_384) |
|
| 352 |
+
| [`mc2_s16_224`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[196, 384]` | 384 | [patches](lcs-1k/vision/mc2_s16_224_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/042_mc2_s16_224.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/mc2_s16_224) |
|
| 353 |
+
| [`mc2_s16_224_mt5`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[196, 384]` | 384 | [patches](lcs-1k/vision/mc2_s16_224_mt5_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/043_mc2_s16_224_mt5.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/mc2_s16_224_mt5) |
|
| 354 |
+
| [`mc2_s16_384`](https://github.com/facebookresearch/MetaCLIP#pre-trained-models) | LANG | `[576, 384]` | 384 | [patches](lcs-1k/vision/mc2_s16_384_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/044_mc2_s16_384.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/mc2_s16_384) |
|
| 355 |
+
| [`pe_core_b16_224`](https://huggingface.co/facebook/PE-Core-B16-224) | LANG | `[196, 768]` | 768 | [patches](lcs-1k/vision/pe_core_b16_224_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/045_pe_core_b16_224.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/pe_core_b16_224) |
|
| 356 |
+
| [`pe_core_g14_448`](https://huggingface.co/facebook/PE-Core-G14-448) | LANG | `[1024, 1536]` | 1536 | [patches](lcs-1k/vision/pe_core_g14_448_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/046_pe_core_g14_448.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/pe_core_g14_448) |
|
| 357 |
+
| [`pe_lang_l14_448`](https://huggingface.co/facebook/PE-Lang-L14-448) | LANG | `[1024, 1024]` | 1024 | [patches](lcs-1k/vision/pe_lang_l14_448_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/047_pe_lang_l14_448.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/pe_lang_l14_448) |
|
| 358 |
+
| [`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](lcs-1k/vision/siglip2_b16_224_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/052_siglip2_b16_224.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/siglip2_b16_224) |
|
| 359 |
+
| [`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](lcs-1k/vision/siglip2_b16_256_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/053_siglip2_b16_256.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/siglip2_b16_256) |
|
| 360 |
+
| [`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](lcs-1k/vision/siglip2_b16_384_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/054_siglip2_b16_384.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/siglip2_b16_384) |
|
| 361 |
+
| [`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](lcs-1k/vision/siglip2_b16_512_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/055_siglip2_b16_512.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/siglip2_b16_512) |
|
| 362 |
+
| [`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](lcs-1k/vision/siglip2_b32_256_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/056_siglip2_b32_256.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/siglip2_b32_256) |
|
| 363 |
+
| [`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](lcs-1k/vision/siglip2_g16_256_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/057_siglip2_g16_256.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/siglip2_g16_256) |
|
| 364 |
+
| [`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](lcs-1k/vision/siglip2_g16_384_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/058_siglip2_g16_384.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/siglip2_g16_384) |
|
| 365 |
+
| [`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](lcs-1k/vision/siglip2_l16_256_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/059_siglip2_l16_256.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/siglip2_l16_256) |
|
| 366 |
+
| [`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](lcs-1k/vision/siglip2_l16_384_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/060_siglip2_l16_384.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/siglip2_l16_384) |
|
| 367 |
+
| [`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](lcs-1k/vision/siglip2_l16_512_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/061_siglip2_l16_512.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/siglip2_l16_512) |
|
| 368 |
+
| [`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](lcs-1k/vision/siglip2_sm14_224_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/062_siglip2_sm14_224.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/siglip2_sm14_224) |
|
| 369 |
+
| [`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](lcs-1k/vision/siglip2_sm14_384_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/063_siglip2_sm14_384.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/siglip2_sm14_384) |
|
| 370 |
+
| [`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](lcs-1k/vision/siglip2_sm16_256_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/064_siglip2_sm16_256.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/siglip2_sm16_256) |
|
| 371 |
+
| [`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](lcs-1k/vision/siglip2_sm16_384_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/065_siglip2_sm16_384.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/siglip2_sm16_384) |
|
| 372 |
+
| [`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](lcs-1k/vision/siglip2_sm16_512_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/066_siglip2_sm16_512.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/siglip2_sm16_512) |
|
| 373 |
+
| [`dino_vitb16`](https://huggingface.co/facebook/dino-vitb16) | SSL | `[196, 768]` | 1536 | [patches](lcs-1k/vision/dino_vitb16_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/002_dino_vitb16.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/dino_vitb16) |
|
| 374 |
+
| [`dino_vitb8`](https://huggingface.co/facebook/dino-vitb8) | SSL | `[784, 768]` | 1536 | [patches](lcs-1k/vision/dino_vitb8_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/003_dino_vitb8.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/dino_vitb8) |
|
| 375 |
+
| [`dino_vits16`](https://huggingface.co/facebook/dino-vits16) | SSL | `[196, 384]` | 768 | [patches](lcs-1k/vision/dino_vits16_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/004_dino_vits16.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/dino_vits16) |
|
| 376 |
+
| [`dino_vits8`](https://huggingface.co/facebook/dino-vits8) | SSL | `[784, 384]` | 768 | [patches](lcs-1k/vision/dino_vits8_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/005_dino_vits8.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/dino_vits8) |
|
| 377 |
+
| [`dinov2_base`](https://huggingface.co/facebook/dinov2-base) | SSL | `[256, 768]` | 1536 | [patches](lcs-1k/vision/dinov2_base_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/006_dinov2_base.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/dinov2_base) |
|
| 378 |
+
| [`dinov2_giant`](https://huggingface.co/facebook/dinov2-giant) | SSL | `[256, 1536]` | 3072 | [patches](lcs-1k/vision/dinov2_giant_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/007_dinov2_giant.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/dinov2_giant) |
|
| 379 |
+
| [`dinov2_large`](https://huggingface.co/facebook/dinov2-large) | SSL | `[256, 1024]` | 2048 | [patches](lcs-1k/vision/dinov2_large_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/008_dinov2_large.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/dinov2_large) |
|
| 380 |
+
| [`dinov2_small`](https://huggingface.co/facebook/dinov2-small) | SSL | `[256, 384]` | 768 | [patches](lcs-1k/vision/dinov2_small_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/009_dinov2_small.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/dinov2_small) |
|
| 381 |
+
| [`dinov3_vitl16`](https://huggingface.co/facebook/dinov3-vitl16-pretrain-lvd1689m) | SSL | `[256, 1024]` | 2048 | [patches](lcs-1k/vision/dinov3_vitl16_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/010_dinov3_vitl16.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/dinov3_vitl16) |
|
| 382 |
+
| [`eupe_convnext_b`](https://huggingface.co/facebook/EUPE-ConvNeXt-B) | SSL | `[64, 1024]` | 1024 | [patches](lcs-1k/vision/eupe_convnext_b_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/016_eupe_convnext_b.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/eupe_convnext_b) |
|
| 383 |
+
| [`eupe_vit_b`](https://huggingface.co/facebook/EUPE-ViT-B) | SSL | `[256, 768]` | 1536 | [patches](lcs-1k/vision/eupe_vit_b_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/017_eupe_vit_b.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/eupe_vit_b) |
|
| 384 |
+
| [`eupe_vit_s`](https://huggingface.co/facebook/EUPE-ViT-S) | SSL | `[256, 384]` | 768 | [patches](lcs-1k/vision/eupe_vit_s_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/018_eupe_vit_s.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/eupe_vit_s) |
|
| 385 |
+
| [`eupe_vit_t`](https://huggingface.co/facebook/EUPE-ViT-T) | SSL | `[256, 192]` | 384 | [patches](lcs-1k/vision/eupe_vit_t_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/019_eupe_vit_t.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/eupe_vit_t) |
|
| 386 |
+
| [`ijepa_vith14`](https://github.com/facebookresearch/ijepa) | SSL | `[256, 1280]` | 1280 | [patches](lcs-1k/vision/ijepa_vith14_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/020_ijepa_vith14.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/ijepa_vith14) |
|
| 387 |
+
| [`pixio_vitb16`](https://huggingface.co/facebook/pixio-vitb16) | SSL | `[256, 768]` | 768 | [patches](lcs-1k/vision/pixio_vitb16_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/048_pixio_vitb16.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/pixio_vitb16) |
|
| 388 |
+
| [`pixio_vith16`](https://huggingface.co/facebook/pixio-vith16) | SSL | `[256, 1280]` | 1280 | [patches](lcs-1k/vision/pixio_vith16_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/049_pixio_vith16.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/pixio_vith16) |
|
| 389 |
+
| [`pixio_vitl16`](https://huggingface.co/facebook/pixio-vitl16) | SSL | `[256, 1024]` | 1024 | [patches](lcs-1k/vision/pixio_vitl16_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/050_pixio_vitl16.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/pixio_vitl16) |
|
| 390 |
+
| [`raev2_dinov3l_k7`](https://huggingface.co/nyu-visionx/RAEv2-models) | SSL | `[256, 1024]` | 1024 | [patches](lcs-1k/vision/raev2_dinov3l_k7_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/051_raev2_dinov3l_k7.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/raev2_dinov3l_k7) |
|
| 391 |
+
| [`webssl_dino1b_full2b_224`](https://huggingface.co/facebook/webssl-dino1b-full2b-224) | SSL | `[256, 1536]` | 1536 | [patches](lcs-1k/vision/webssl_dino1b_full2b_224_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/067_webssl_dino1b_full2b_224.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/webssl_dino1b_full2b_224) |
|
| 392 |
+
| [`webssl_mae1b_full2b_224`](https://huggingface.co/facebook/webssl-mae1b-full2b-224) | SSL | `[256, 1536]` | 1536 | [patches](lcs-1k/vision/webssl_mae1b_full2b_224_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/068_webssl_mae1b_full2b_224.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/webssl_mae1b_full2b_224) |
|
| 393 |
+
| [`webssl_mae300m_full2b_224`](https://huggingface.co/facebook/webssl-mae300m-full2b-224) | SSL | `[196, 1024]` | 1024 | [patches](lcs-1k/vision/webssl_mae300m_full2b_224_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/069_webssl_mae300m_full2b_224.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/webssl_mae300m_full2b_224) |
|
| 394 |
+
| [`webssl_mae3b_full2b_224`](https://huggingface.co/facebook/webssl-mae3b-full2b-224) | SSL | `[256, 3072]` | 3072 | [patches](lcs-1k/vision/webssl_mae3b_full2b_224_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/070_webssl_mae3b_full2b_224.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Features/tree/main/lcs-10k/vision/webssl_mae3b_full2b_224) |
|
| 395 |
|
| 396 |
## Dataset Creation and Provenance
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