Document paired visual/text and ImageNet encoder feature datasets

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+}

LICENSES.md

@@ -0,0 +1,13 @@
+# Upstream data and model terms
+
+This repository assembles precomputed representations and sample metadata from multiple sources. The artifacts do not have a single newly assigned blanket license. The applicable upstream dataset and encoder terms continue to govern reuse.
+
+| Component | Source and terms to consult |
+| --- | --- |
+| LCS-558K sample captions and image identifiers | [LLaVA-Pretrain dataset card](https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain) and [LLaVA project](https://github.com/haotian-liu/LLaVA) |
+| ImageNet-1K sample labels, identifiers, and derived features | [ImageNet access and source terms](https://www.image-net.org/download.php) and [official ImageNet-1K dataset card](https://huggingface.co/datasets/ILSVRC/imagenet-1k) |
+| Visual features | Source-data terms and the individual encoder/model terms linked in [encoders.json](encoders.json) and the [Model Zoo](https://huggingface.co/336labs/VisionEncoder-to-MLLM-ModelZoo) |
+| Text features | Source-data terms and [Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct), [Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B), and [SmolLM2-1.7B-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct) model cards |
+| Evaluation software | [MLLM-VisionEncoder-Eval source repository](https://github.com/JuntaoTang/MLLM-VisionEncoder-Eval) and its applicable license |
+
+Raw source images and encoder weights are obtained from their respective upstream releases. Listing upstream download links does not override access conditions or grant new redistribution rights. Cite the paper, source datasets, and the encoders used in your work.

README.md

@@ -0,0 +1,324 @@
+---
+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: 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:** **Upload in progress.** The full expected inventory is in `FILES.json`; feature files 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 |
+| ImageNet200k pooled features | 70 | `[200000, D]` | float32 | 59.89 |
+
+Total prepared contents, including shared metadata and protocols: approximately **115.21 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
+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.
+
+### ImageNet-1K: 200,000 training examples
+
+The subset contains **200 examples per class**, across **1,000 classes**, from the official ImageNet-1K training split. 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 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")
+imagenet_samples = load_dataset("336labs/VisionEncoder-Features",
+                                "imagenet1k_train200k", split="train")
+```
+
+The two 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 and ImageNet array paths, metadata, shapes, and upstream encoder sources. 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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+| [`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) |
+
+## 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 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. 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:** the published 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 is a small captioned-image sample; 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:** raw images, complete encoder weights, MLLM checkpoints, PCA/distance caches, and larger-sample feature experiments are available through their respective upstream resources or project releases.
+
+## 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).

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encoders.json

@@ -0,0 +1,2736 @@
+{
+  "encoders": [
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+        ],
+        "auxiliary_weights": [],
+        "code": [
+          "https://github.com/openai/CLIP"
+        ],
+        "download_notes": "Official Hugging Face model; downloading the full repository is safer when config files are required."
+      },
+      "lcs_patch": {
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+        "metadata": "lcs558k/n1000_seed42/vision/clip_openai__l14_patch_n1000_seed42.json",
+        "shape": [
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+          256,
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+        "metadata": "imagenet1k/train200k/metadata/001_clip_openai__l14.json",
+        "shape": [
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+          1024
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+        "representation": "final post-LayerNorm CLS before the OpenAI CLIP 1024-to-768 visual projection"
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+        "display_name": "DINO ViT-B/16",
+        "upstream_page": [
+          "https://huggingface.co/facebook/dino-vitb16"
+        ],
+        "weights": [
+          "https://huggingface.co/facebook/dino-vitb16/resolve/main/pytorch_model.bin?download=true"
+        ],
+        "auxiliary_weights": [],
+        "code": [
+          "https://github.com/facebookresearch/dino"
+        ],
+        "download_notes": "Official Hugging Face model; downloading the full repository is safer when config files are required."
+      },
+      "lcs_patch": {
+        "file": "lcs558k/n1000_seed42/vision/dino_vitb16_patch_n1000_seed42.npy",
+        "metadata": "lcs558k/n1000_seed42/vision/dino_vitb16_patch_n1000_seed42.json",
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+          768
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+        "metadata": "imagenet1k/train200k/metadata/002_dino_vitb16.json",
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+        "weights": [
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+        ],
+        "auxiliary_weights": [],
+        "code": [
+          "https://github.com/facebookresearch/dino"
+        ],
+        "download_notes": "Official Hugging Face model; downloading the full repository is safer when config files are required."
+      },
+      "lcs_patch": {
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+        "metadata": "lcs558k/n1000_seed42/vision/dino_vitb8_patch_n1000_seed42.json",
+        "shape": [
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+          768
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+        "feature_layer_provenance": "not_recorded_in_original_audit"
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+        "metadata": "imagenet1k/train200k/metadata/003_dino_vitb8.json",
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+        "display_name": "DINO ViT-S/16",
+        "upstream_page": [
+          "https://huggingface.co/facebook/dino-vits16"
+        ],
+        "weights": [
+          "https://huggingface.co/facebook/dino-vits16/resolve/main/pytorch_model.bin?download=true"
+        ],
+        "auxiliary_weights": [],
+        "code": [
+          "https://github.com/facebookresearch/dino"
+        ],
+        "download_notes": "Official Hugging Face model; downloading the full repository is safer when config files are required."
+      },
+      "lcs_patch": {
+        "file": "lcs558k/n1000_seed42/vision/dino_vits16_patch_n1000_seed42.npy",
+        "metadata": "lcs558k/n1000_seed42/vision/dino_vits16_patch_n1000_seed42.json",
+        "shape": [
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+        "dtype": "float16",
+        "feature_layer_provenance": "not_recorded_in_original_audit"
+      },
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+        "metadata": "imagenet1k/train200k/metadata/004_dino_vits16.json",
+        "shape": [
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+          768
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+        "representation": "concat(final normalized CLS, mean(final normalized patch tokens)); excludes 0 register token(s)"
+      }
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+      "source": {
+        "category": "SSL",
+        "display_name": "DINO ViT-S/8",
+        "upstream_page": [
+          "https://huggingface.co/facebook/dino-vits8"
+        ],
+        "weights": [
+          "https://huggingface.co/facebook/dino-vits8/resolve/main/model.safetensors?download=true"
+        ],
+        "auxiliary_weights": [],
+        "code": [
+          "https://github.com/facebookresearch/dino"
+        ],
+        "download_notes": "Official Hugging Face model; downloading the full repository is safer when config files are required."
+      },
+      "lcs_patch": {
+        "file": "lcs558k/n1000_seed42/vision/dino_vits8_patch_n1000_seed42.npy",
+        "metadata": "lcs558k/n1000_seed42/vision/dino_vits8_patch_n1000_seed42.json",
+        "shape": [
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+          384
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+        "dtype": "float16",
+        "feature_layer_provenance": "not_recorded_in_original_audit"
+      },
+      "imagenet_pooled": {
+        "file": "imagenet1k/train200k/features/005_dino_vits8.npy",
+        "metadata": "imagenet1k/train200k/metadata/005_dino_vits8.json",
+        "shape": [
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+          768
+        ],
+        "dtype": "float32",
+        "representation": "concat(final normalized CLS, mean(final normalized patch tokens)); excludes 0 register token(s)"
+      }
+    },
+    {
+      "tokenizer_id": "dinov2_base",
+      "source": {
+        "category": "SSL",
+        "display_name": "DINOv2 ViT-B/14",
+        "upstream_page": [
+          "https://huggingface.co/facebook/dinov2-base"
+        ],
+        "weights": [
+          "https://huggingface.co/facebook/dinov2-base/resolve/main/model.safetensors?download=true"
+        ],
+        "auxiliary_weights": [],
+        "code": [
+          "https://github.com/facebookresearch/dinov2"
+        ],
+        "download_notes": "Official Hugging Face model; downloading the full repository is safer when config files are required."
+      },
+      "lcs_patch": {
+        "file": "lcs558k/n1000_seed42/vision/dinov2_base_patch_n1000_seed42.npy",
+        "metadata": "lcs558k/n1000_seed42/vision/dinov2_base_patch_n1000_seed42.json",
+        "shape": [
+          1000,
+          256,
+          768
+        ],
+        "dtype": "float16",
+        "feature_layer_provenance": "not_recorded_in_original_audit"
+      },
+      "imagenet_pooled": {
+        "file": "imagenet1k/train200k/features/006_dinov2_base.npy",
+        "metadata": "imagenet1k/train200k/metadata/006_dinov2_base.json",
+        "shape": [
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+          1536
+        ],
+        "dtype": "float32",
+        "representation": "concat(final normalized CLS, mean(final normalized patch tokens)); excludes 0 register token(s)"
+      }
+    },
+    {
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+      "source": {
+        "category": "SSL",
+        "display_name": "DINOv2 ViT-G/14",
+        "upstream_page": [
+          "https://huggingface.co/facebook/dinov2-giant"
+        ],
+        "weights": [
+          "https://huggingface.co/facebook/dinov2-giant/resolve/main/model.safetensors?download=true"
+        ],
+        "auxiliary_weights": [],
+        "code": [
+          "https://github.com/facebookresearch/dinov2"
+        ],
+        "download_notes": "Official Hugging Face model; downloading the full repository is safer when config files are required."
+      },
+      "lcs_patch": {
+        "file": "lcs558k/n1000_seed42/vision/dinov2_giant_patch_n1000_seed42.npy",
+        "metadata": "lcs558k/n1000_seed42/vision/dinov2_giant_patch_n1000_seed42.json",
+        "shape": [
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+        "dtype": "float16",
+        "feature_layer_provenance": "not_recorded_in_original_audit"
+      },
+      "imagenet_pooled": {
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+        "metadata": "imagenet1k/train200k/metadata/007_dinov2_giant.json",
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+        "dtype": "float32",
+        "representation": "concat(final normalized CLS, mean(final normalized patch tokens)); excludes 0 register token(s)"
+      }
+    },
+    {
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+      "source": {
+        "category": "SSL",
+        "display_name": "DINOv2 ViT-L/14",
+        "upstream_page": [
+          "https://huggingface.co/facebook/dinov2-large"
+        ],
+        "weights": [
+          "https://huggingface.co/facebook/dinov2-large/resolve/main/model.safetensors?download=true"
+        ],
+        "auxiliary_weights": [],
+        "code": [
+          "https://github.com/facebookresearch/dinov2"
+        ],
+        "download_notes": "Official Hugging Face model; downloading the full repository is safer when config files are required."
+      },
+      "lcs_patch": {
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+        "metadata": "lcs558k/n1000_seed42/vision/dinov2_large_patch_n1000_seed42.json",
+        "shape": [
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+        "dtype": "float16",
+        "feature_layer_provenance": "not_recorded_in_original_audit"
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+        "metadata": "imagenet1k/train200k/metadata/008_dinov2_large.json",
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+        "representation": "concat(final normalized CLS, mean(final normalized patch tokens)); excludes 0 register token(s)"
+      }
+    },
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+      "source": {
+        "category": "SSL",
+        "display_name": "DINOv2 ViT-S/14",
+        "upstream_page": [
+          "https://huggingface.co/facebook/dinov2-small"
+        ],
+        "weights": [
+          "https://huggingface.co/facebook/dinov2-small/resolve/main/model.safetensors?download=true"
+        ],
+        "auxiliary_weights": [],
+        "code": [
+          "https://github.com/facebookresearch/dinov2"
+        ],
+        "download_notes": "Official Hugging Face model; downloading the full repository is safer when config files are required."
+      },
+      "lcs_patch": {
+        "file": "lcs558k/n1000_seed42/vision/dinov2_small_patch_n1000_seed42.npy",
+        "metadata": "lcs558k/n1000_seed42/vision/dinov2_small_patch_n1000_seed42.json",
+        "shape": [
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+        "feature_layer_provenance": "not_recorded_in_original_audit"
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+        "metadata": "imagenet1k/train200k/metadata/009_dinov2_small.json",
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+        "representation": "concat(final normalized CLS, mean(final normalized patch tokens)); excludes 0 register token(s)"
+      }
+    },
+    {
+      "tokenizer_id": "dinov3_vitl16",
+      "source": {
+        "category": "SSL",
+        "display_name": "DINOv3 ViT-L/16",
+        "upstream_page": [
+          "https://huggingface.co/facebook/dinov3-vitl16-pretrain-lvd1689m"
+        ],
+        "weights": [
+          "https://huggingface.co/facebook/dinov3-vitl16-pretrain-lvd1689m/resolve/main/model.safetensors?download=true"
+        ],
+        "auxiliary_weights": [],
+        "code": [
+          "https://github.com/facebookresearch/dinov3"
+        ],
+        "download_notes": "Gated: accept Meta's DINOv3 terms and authenticate. The experiment used the equivalent original .pth checkpoint named dinov3_vitl16_pretrain_lvd1689m-8aa4cbdd.pth."
+      },
+      "lcs_patch": {
+        "file": "lcs558k/n1000_seed42/vision/dinov3_vitl16_patch_n1000_seed42.npy",
+        "metadata": "lcs558k/n1000_seed42/vision/dinov3_vitl16_patch_n1000_seed42.json",
+        "shape": [
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+        "metadata": "imagenet1k/train200k/metadata/010_dinov3_vitl16.json",
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+      }
+    },
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+      "source": {
+        "category": "SSL",
+        "display_name": "EUPE ConvNeXt-B",
+        "upstream_page": [
+          "https://huggingface.co/facebook/EUPE-ConvNeXt-B"
+        ],
+        "weights": [
+          "https://huggingface.co/facebook/EUPE-ConvNeXt-B/resolve/main/EUPE-ConvNeXt-B.pt?download=true"
+        ],
+        "auxiliary_weights": [],
+        "code": [
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+        "download_notes": "Official Hugging Face model; downloading the full repository is safer when config files are required."
+      },
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+        "metadata": "lcs558k/n1000_seed42/vision/eupe_convnext_b_patch_n1000_seed42.json",
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+        "metadata": "imagenet1k/train200k/metadata/016_eupe_convnext_b.json",
+        "shape": [
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+      }
+    },
+    {
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+      "imagenet_pooled": {
+        "file": "imagenet1k/train200k/features/061_siglip2_l16_512.npy",
+        "metadata": "imagenet1k/train200k/metadata/061_siglip2_l16_512.json",
+        "shape": [
+          200000,
+          1024
+        ],
+        "dtype": "float32",
+        "representation": "native SigLIP 2 model(images) [B,D] output after MAP attention pooling"
+      }
+    },
+    {
+      "tokenizer_id": "siglip2_sm14_224",
+      "source": {
+        "category": "LANG",
+        "display_name": "SigLIP2 So400m/l14 (224)",
+        "upstream_page": [
+          "https://github.com/google-research/big_vision/blob/main/big_vision/configs/proj/image_text/README_siglip2.md"
+        ],
+        "weights": [
+          "https://storage.googleapis.com/big_vision/siglip2/siglip2_so400m14_224.npz"
+        ],
+        "auxiliary_weights": [],
+        "code": [
+          "https://github.com/google-research/big_vision"
+        ],
+        "download_notes": "Official direct checkpoint URL."
+      },
+      "lcs_patch": {
+        "file": "lcs558k/n1000_seed42/vision/siglip2_sm14_224_patch_n1000_seed42.npy",
+        "metadata": "lcs558k/n1000_seed42/vision/siglip2_sm14_224_patch_n1000_seed42.json",
+        "shape": [
+          1000,
+          256,
+          1152
+        ],
+        "dtype": "float16",
+        "feature_layer_provenance": "not_recorded_in_original_audit"
+      },
+      "imagenet_pooled": {
+        "file": "imagenet1k/train200k/features/062_siglip2_sm14_224.npy",
+        "metadata": "imagenet1k/train200k/metadata/062_siglip2_sm14_224.json",
+        "shape": [
+          200000,
+          1152
+        ],
+        "dtype": "float32",
+        "representation": "native SigLIP 2 model(images) [B,D] output after MAP attention pooling"
+      }
+    },
+    {
+      "tokenizer_id": "siglip2_sm14_384",
+      "source": {
+        "category": "LANG",
+        "display_name": "SigLIP2 So400m/l14 (384)",
+        "upstream_page": [
+          "https://github.com/google-research/big_vision/blob/main/big_vision/configs/proj/image_text/README_siglip2.md"
+        ],
+        "weights": [
+          "https://storage.googleapis.com/big_vision/siglip2/siglip2_so400m14_384.npz"
+        ],
+        "auxiliary_weights": [],
+        "code": [
+          "https://github.com/google-research/big_vision"
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+        "download_notes": "Official direct checkpoint URL."
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+        "file": "lcs558k/n1000_seed42/vision/siglip2_sm14_384_patch_n1000_seed42.npy",
+        "metadata": "lcs558k/n1000_seed42/vision/siglip2_sm14_384_patch_n1000_seed42.json",
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+          729,
+          1152
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+        "dtype": "float16",
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+      "imagenet_pooled": {
+        "file": "imagenet1k/train200k/features/063_siglip2_sm14_384.npy",
+        "metadata": "imagenet1k/train200k/metadata/063_siglip2_sm14_384.json",
+        "shape": [
+          200000,
+          1152
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+        "dtype": "float32",
+        "representation": "native SigLIP 2 model(images) [B,D] output after MAP attention pooling"
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+    },
+    {
+      "tokenizer_id": "siglip2_sm16_256",
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+        "category": "LANG",
+        "display_name": "SigLIP2 So400m/l16 (256)",
+        "upstream_page": [
+          "https://github.com/google-research/big_vision/blob/main/big_vision/configs/proj/image_text/README_siglip2.md"
+        ],
+        "weights": [
+          "https://storage.googleapis.com/big_vision/siglip2/siglip2_so400m16_256.npz"
+        ],
+        "auxiliary_weights": [],
+        "code": [
+          "https://github.com/google-research/big_vision"
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+        "download_notes": "Official direct checkpoint URL."
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+        "file": "lcs558k/n1000_seed42/vision/siglip2_sm16_256_patch_n1000_seed42.npy",
+        "metadata": "lcs558k/n1000_seed42/vision/siglip2_sm16_256_patch_n1000_seed42.json",
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+          256,
+          1152
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+        "dtype": "float16",
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+      "imagenet_pooled": {
+        "file": "imagenet1k/train200k/features/064_siglip2_sm16_256.npy",
+        "metadata": "imagenet1k/train200k/metadata/064_siglip2_sm16_256.json",
+        "shape": [
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+          1152
+        ],
+        "dtype": "float32",
+        "representation": "native SigLIP 2 model(images) [B,D] output after MAP attention pooling"
+      }
+    },
+    {
+      "tokenizer_id": "siglip2_sm16_384",
+      "source": {
+        "category": "LANG",
+        "display_name": "SigLIP2 So400m/l16 (384)",
+        "upstream_page": [
+          "https://github.com/google-research/big_vision/blob/main/big_vision/configs/proj/image_text/README_siglip2.md"
+        ],
+        "weights": [
+          "https://storage.googleapis.com/big_vision/siglip2/siglip2_so400m16_384.npz"
+        ],
+        "auxiliary_weights": [],
+        "code": [
+          "https://github.com/google-research/big_vision"
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+        "download_notes": "Official direct checkpoint URL."
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+      "lcs_patch": {
+        "file": "lcs558k/n1000_seed42/vision/siglip2_sm16_384_patch_n1000_seed42.npy",
+        "metadata": "lcs558k/n1000_seed42/vision/siglip2_sm16_384_patch_n1000_seed42.json",
+        "shape": [
+          1000,
+          576,
+          1152
+        ],
+        "dtype": "float16",
+        "feature_layer_provenance": "not_recorded_in_original_audit"
+      },
+      "imagenet_pooled": {
+        "file": "imagenet1k/train200k/features/065_siglip2_sm16_384.npy",
+        "metadata": "imagenet1k/train200k/metadata/065_siglip2_sm16_384.json",
+        "shape": [
+          200000,
+          1152
+        ],
+        "dtype": "float32",
+        "representation": "native SigLIP 2 model(images) [B,D] output after MAP attention pooling"
+      }
+    },
+    {
+      "tokenizer_id": "siglip2_sm16_512",
+      "source": {
+        "category": "LANG",
+        "display_name": "SigLIP2 So400m/l16 (512)",
+        "upstream_page": [
+          "https://github.com/google-research/big_vision/blob/main/big_vision/configs/proj/image_text/README_siglip2.md"
+        ],
+        "weights": [
+          "https://storage.googleapis.com/big_vision/siglip2/siglip2_so400m16_512.npz"
+        ],
+        "auxiliary_weights": [],
+        "code": [
+          "https://github.com/google-research/big_vision"
+        ],
+        "download_notes": "Official direct checkpoint URL."
+      },
+      "lcs_patch": {
+        "file": "lcs558k/n1000_seed42/vision/siglip2_sm16_512_patch_n1000_seed42.npy",
+        "metadata": "lcs558k/n1000_seed42/vision/siglip2_sm16_512_patch_n1000_seed42.json",
+        "shape": [
+          1000,
+          1024,
+          1152
+        ],
+        "dtype": "float16",
+        "feature_layer_provenance": "not_recorded_in_original_audit"
+      },
+      "imagenet_pooled": {
+        "file": "imagenet1k/train200k/features/066_siglip2_sm16_512.npy",
+        "metadata": "imagenet1k/train200k/metadata/066_siglip2_sm16_512.json",
+        "shape": [
+          200000,
+          1152
+        ],
+        "dtype": "float32",
+        "representation": "native SigLIP 2 model(images) [B,D] output after MAP attention pooling"
+      }
+    },
+    {
+      "tokenizer_id": "toklip_l_384",
+      "source": {
+        "category": "DISCRETE",
+        "display_name": "TokLIP-L (384)",
+        "upstream_page": [
+          "https://huggingface.co/TencentARC/TokLIP"
+        ],
+        "weights": [
+          "https://huggingface.co/TencentARC/TokLIP/resolve/main/TokLIP_L_384.pt?download=true"
+        ],
+        "auxiliary_weights": [
+          "https://huggingface.co/peizesun/llamagen_t2i/resolve/main/vq_ds16_t2i.pt?download=true"
+        ],
+        "code": [
+          "https://github.com/TencentARC/TokLIP"
+        ],
+        "download_notes": "Download TokLIP_L_384.pt and the shared auxiliary tokenizer weight vq_ds16_t2i.pt."
+      },
+      "lcs_patch": {
+        "file": "lcs558k/n1000_seed42/vision/toklip_l_384_patch_n1000_seed42.npy",
+        "metadata": "lcs558k/n1000_seed42/vision/toklip_l_384_patch_n1000_seed42.json",
+        "shape": [
+          1000,
+          576,
+          1152
+        ],
+        "dtype": "float16",
+        "feature_layer_provenance": "not_recorded_in_original_audit"
+      },
+      "imagenet_pooled": {
+        "file": "imagenet1k/train200k/features/011_toklip_l_384.npy",
+        "metadata": "imagenet1k/train200k/metadata/011_toklip_l_384.json",
+        "shape": [
+          200000,
+          1152
+        ],
+        "dtype": "float32",
+        "representation": "mean of final normalized TokLIP semantic tokens; forward_head is not used"
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+    },
+    {
+      "tokenizer_id": "toklip_s_256",
+      "source": {
+        "category": "DISCRETE",
+        "display_name": "TokLIP-S (256)",
+        "upstream_page": [
+          "https://huggingface.co/TencentARC/TokLIP"
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+        "weights": [
+          "https://huggingface.co/TencentARC/TokLIP/resolve/main/TokLIP_S_256.pt?download=true"
+        ],
+        "auxiliary_weights": [
+          "https://huggingface.co/peizesun/llamagen_t2i/resolve/main/vq_ds16_t2i.pt?download=true"
+        ],
+        "code": [
+          "https://github.com/TencentARC/TokLIP"
+        ],
+        "download_notes": "Download TokLIP_S_256.pt and the shared auxiliary tokenizer weight vq_ds16_t2i.pt."
+      },
+      "lcs_patch": {
+        "file": "lcs558k/n1000_seed42/vision/toklip_s_256_patch_n1000_seed42.npy",
+        "metadata": "lcs558k/n1000_seed42/vision/toklip_s_256_patch_n1000_seed42.json",
+        "shape": [
+          1000,
+          256,
+          1152
+        ],
+        "dtype": "float16",
+        "feature_layer_provenance": "not_recorded_in_original_audit"
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+      "imagenet_pooled": {
+        "file": "imagenet1k/train200k/features/012_toklip_s_256.npy",
+        "metadata": "imagenet1k/train200k/metadata/012_toklip_s_256.json",
+        "shape": [
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+          1152
+        ],
+        "dtype": "float32",
+        "representation": "mean of final normalized TokLIP semantic tokens; forward_head is not used"
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+    },
+    {
+      "tokenizer_id": "uniar_bsq",
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+        "display_name": "UniAR-BSQ",
+        "upstream_page": [
+          "https://huggingface.co/ShareLab-SII/UniAR-SFT/tree/main/bsq_encoder"
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+        "weights": [
+          "https://huggingface.co/ShareLab-SII/UniAR-SFT/resolve/main/bsq_encoder/model.safetensors?download=true"
+        ],
+        "auxiliary_weights": [],
+        "code": [
+          "https://github.com/ShareLab-SII/UniAR"
+        ],
+        "download_notes": "Current official public location. The older local registry recorded FoundationVision/UniAR; keep the full bsq_encoder directory, not only model.safetensors."
+      },
+      "lcs_patch": {
+        "file": "lcs558k/n1000_seed42/vision/uniar_bsq_patch_n1000_seed42.npy",
+        "metadata": "lcs558k/n1000_seed42/vision/uniar_bsq_patch_n1000_seed42.json",
+        "shape": [
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+          1024,
+          4608
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+        "dtype": "float16",
+        "feature_layer_provenance": "legacy_BSQ_deepstack_concatenation_documented_in_recovery"
+      },
+      "imagenet_pooled": {
+        "file": "imagenet1k/train200k/features/013_uniar_bsq.npy",
+        "metadata": "imagenet1k/train200k/metadata/013_uniar_bsq.json",
+        "shape": [
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+          4096
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+        "dtype": "float32",
+        "representation": "mean of UniAR block 27 tokens after 64-bit BSQ quantize/dequantize, BSQ output projection, and the official 2x2 patch merger"
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+    },
+    {
+      "tokenizer_id": "unitok_attn",
+      "source": {
+        "category": "DISCRETE",
+        "display_name": "UniTok-Attn (256)",
+        "upstream_page": [
+          "https://huggingface.co/FoundationVision/unitok_tokenizer"
+        ],
+        "weights": [
+          "https://huggingface.co/FoundationVision/unitok_tokenizer/resolve/main/unitok_tokenizer.pth?download=true"
+        ],
+        "auxiliary_weights": [],
+        "code": [
+          "https://github.com/FoundationVision/UniTok"
+        ],
+        "download_notes": "Official Hugging Face checkpoint file."
+      },
+      "lcs_patch": {
+        "file": "lcs558k/n1000_seed42/vision/unitok_attn_patch_n1000_seed42.npy",
+        "metadata": "lcs558k/n1000_seed42/vision/unitok_attn_patch_n1000_seed42.json",
+        "shape": [
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+          256,
+          1024
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+        "dtype": "float16",
+        "feature_layer_provenance": "not_recorded_in_original_audit"
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+        "file": "imagenet1k/train200k/features/014_unitok_attn.npy",
+        "metadata": "imagenet1k/train200k/metadata/014_unitok_attn.json",
+        "shape": [
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+          1024
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+        "dtype": "float32",
+        "representation": "quantize/dequantize -> post_quant_proj -> token mean -> fc_norm, before UniTok projection"
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+    },
+    {
+      "tokenizer_id": "vilau_256",
+      "source": {
+        "category": "DISCRETE",
+        "display_name": "VILA-U (256)",
+        "upstream_page": [
+          "https://huggingface.co/mit-han-lab/vila-u-7b-256"
+        ],
+        "weights": [
+          "https://huggingface.co/mit-han-lab/vila-u-7b-256/resolve/main/vision_tower/model.safetensors?download=true"
+        ],
+        "auxiliary_weights": [],
+        "code": [
+          "https://github.com/mit-han-lab/vila-u"
+        ],
+        "download_notes": "Official Hugging Face model; downloading the full repository is safer when config files are required."
+      },
+      "lcs_patch": {
+        "file": "lcs558k/n1000_seed42/vision/vilau_256_patch_n1000_seed42.npy",
+        "metadata": "lcs558k/n1000_seed42/vision/vilau_256_patch_n1000_seed42.json",
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+          256,
+          1024
+        ],
+        "dtype": "float16",
+        "feature_layer_provenance": "not_recorded_in_original_audit"
+      },
+      "imagenet_pooled": {
+        "file": "imagenet1k/train200k/features/015_vilau_256.npy",
+        "metadata": "imagenet1k/train200k/metadata/015_vilau_256.json",
+        "shape": [
+          200000,
+          1024
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+        "dtype": "float32",
+        "representation": "mean of the penultimate VILA-U SigLIP encoder-block tokens"
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+    },
+    {
+      "tokenizer_id": "webssl_dino1b_full2b_224",
+      "source": {
+        "category": "SSL",
+        "display_name": "Web-SSL DINO 1B (224)",
+        "upstream_page": [
+          "https://huggingface.co/facebook/webssl-dino1b-full2b-224"
+        ],
+        "weights": [],
+        "auxiliary_weights": [],
+        "code": [
+          "https://github.com/facebookresearch/web-ssl"
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+        "download_notes": "Multi-file Hugging Face repository; download the complete snapshot."
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+      "lcs_patch": {
+        "file": "lcs558k/n1000_seed42/vision/webssl_dino1b_full2b_224_patch_n1000_seed42.npy",
+        "metadata": "lcs558k/n1000_seed42/vision/webssl_dino1b_full2b_224_patch_n1000_seed42.json",
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+        "feature_layer_provenance": "not_recorded_in_original_audit"
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+      "imagenet_pooled": {
+        "file": "imagenet1k/train200k/features/067_webssl_dino1b_full2b_224.npy",
+        "metadata": "imagenet1k/train200k/metadata/067_webssl_dino1b_full2b_224.json",
+        "shape": [
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+        "dtype": "float32",
+        "representation": "final normalized WebSSL-DINO encoder CLS token"
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+    },
+    {
+      "tokenizer_id": "webssl_mae1b_full2b_224",
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+        "display_name": "Web-SSL MAE 1B (224)",
+        "upstream_page": [
+          "https://huggingface.co/facebook/webssl-mae1b-full2b-224"
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+        "weights": [
+          "https://huggingface.co/facebook/webssl-mae1b-full2b-224/resolve/main/model.safetensors?download=true"
+        ],
+        "auxiliary_weights": [],
+        "code": [
+          "https://github.com/facebookresearch/web-ssl"
+        ],
+        "download_notes": "Official Hugging Face model; downloading the full repository is safer when config files are required."
+      },
+      "lcs_patch": {
+        "file": "lcs558k/n1000_seed42/vision/webssl_mae1b_full2b_224_patch_n1000_seed42.npy",
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+        "dtype": "float16",
+        "feature_layer_provenance": "not_recorded_in_original_audit"
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+      "imagenet_pooled": {
+        "file": "imagenet1k/train200k/features/068_webssl_mae1b_full2b_224.npy",
+        "metadata": "imagenet1k/train200k/metadata/068_webssl_mae1b_full2b_224.json",
+        "shape": [
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+          1536
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+        "dtype": "float32",
+        "representation": "final normalized WebSSL-MAE encoder CLS token"
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+    },
+    {
+      "tokenizer_id": "webssl_mae300m_full2b_224",
+      "source": {
+        "category": "SSL",
+        "display_name": "Web-SSL MAE 300M (224)",
+        "upstream_page": [
+          "https://huggingface.co/facebook/webssl-mae300m-full2b-224"
+        ],
+        "weights": [
+          "https://huggingface.co/facebook/webssl-mae300m-full2b-224/resolve/main/model.safetensors?download=true"
+        ],
+        "auxiliary_weights": [],
+        "code": [
+          "https://github.com/facebookresearch/web-ssl"
+        ],
+        "download_notes": "Official Hugging Face model; downloading the full repository is safer when config files are required."
+      },
+      "lcs_patch": {
+        "file": "lcs558k/n1000_seed42/vision/webssl_mae300m_full2b_224_patch_n1000_seed42.npy",
+        "metadata": "lcs558k/n1000_seed42/vision/webssl_mae300m_full2b_224_patch_n1000_seed42.json",
+        "shape": [
+          1000,
+          196,
+          1024
+        ],
+        "dtype": "float16",
+        "feature_layer_provenance": "not_recorded_in_original_audit"
+      },
+      "imagenet_pooled": {
+        "file": "imagenet1k/train200k/features/069_webssl_mae300m_full2b_224.npy",
+        "metadata": "imagenet1k/train200k/metadata/069_webssl_mae300m_full2b_224.json",
+        "shape": [
+          200000,
+          1024
+        ],
+        "dtype": "float32",
+        "representation": "final normalized WebSSL-MAE encoder CLS token"
+      }
+    },
+    {
+      "tokenizer_id": "webssl_mae3b_full2b_224",
+      "source": {
+        "category": "SSL",
+        "display_name": "Web-SSL MAE 3B (224)",
+        "upstream_page": [
+          "https://huggingface.co/facebook/webssl-mae3b-full2b-224"
+        ],
+        "weights": [],
+        "auxiliary_weights": [],
+        "code": [
+          "https://github.com/facebookresearch/web-ssl"
+        ],
+        "download_notes": "Sharded or multi-file Hugging Face model; download the complete repository snapshot."
+      },
+      "lcs_patch": {
+        "file": "lcs558k/n1000_seed42/vision/webssl_mae3b_full2b_224_patch_n1000_seed42.npy",
+        "metadata": "lcs558k/n1000_seed42/vision/webssl_mae3b_full2b_224_patch_n1000_seed42.json",
+        "shape": [
+          1000,
+          256,
+          3072
+        ],
+        "dtype": "float16",
+        "feature_layer_provenance": "not_recorded_in_original_audit"
+      },
+      "imagenet_pooled": {
+        "file": "imagenet1k/train200k/features/070_webssl_mae3b_full2b_224.npy",
+        "metadata": "imagenet1k/train200k/metadata/070_webssl_mae3b_full2b_224.json",
+        "shape": [
+          200000,
+          3072
+        ],
+        "dtype": "float32",
+        "representation": "final normalized WebSSL-MAE encoder CLS token"
+      }
+    }
+  ],
+  "count": 70
+}

imagenet1k/train200k/export_manifest.json

@@ -0,0 +1,1001 @@
+{
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+  "labels_file": "labels.npy",
+  "row_order": "shared source_indices.npy; strictly increasing official ImageNet train indices",
+  "rows_per_tokenizer": 200000,
+  "seed_cache_metadata": "source-local:metadata.json",
+  "shots_per_class": 200,
+  "source_indices_file": "source_indices.npy",
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+  "support_seed": 0,
+  "tokenizer_count": 70,
+  "release": {
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+    "paths_relative_to": "imagenet1k/train200k",
+    "row_order_indices_sha256": "200d9a05c37f0e816cfed2d5c1a0bea55550fa0b2a9c79f874f9a86c472ce360"
+  }
+}

imagenet1k/train200k/manifest.tsv

@@ -0,0 +1,71 @@
+rank	tokenizer	model	feature_dim	dtype	rows	feature_file	representation
+1	clip_openai__l14	clip_openai__l14	1024	float32	200000	features/001_clip_openai__l14.npy	final post-LayerNorm CLS before the OpenAI CLIP 1024-to-768 visual projection
+2	dino_vitb16	dinov1_vitb16	1536	float32	200000	features/002_dino_vitb16.npy	concat(final normalized CLS, mean(final normalized patch tokens)); excludes 0 register token(s)
+3	dino_vitb8	dinov1_vitb8	1536	float32	200000	features/003_dino_vitb8.npy	concat(final normalized CLS, mean(final normalized patch tokens)); excludes 0 register token(s)
+4	dino_vits16	dinov1_vits16	768	float32	200000	features/004_dino_vits16.npy	concat(final normalized CLS, mean(final normalized patch tokens)); excludes 0 register token(s)
+5	dino_vits8	dinov1_vits8	768	float32	200000	features/005_dino_vits8.npy	concat(final normalized CLS, mean(final normalized patch tokens)); excludes 0 register token(s)
+6	dinov2_base	dinov2_base	1536	float32	200000	features/006_dinov2_base.npy	concat(final normalized CLS, mean(final normalized patch tokens)); excludes 0 register token(s)
+7	dinov2_giant	dinov2_giant	3072	float32	200000	features/007_dinov2_giant.npy	concat(final normalized CLS, mean(final normalized patch tokens)); excludes 0 register token(s)
+8	dinov2_large	dinov2_large	2048	float32	200000	features/008_dinov2_large.npy	concat(final normalized CLS, mean(final normalized patch tokens)); excludes 0 register token(s)
+9	dinov2_small	dinov2_small	768	float32	200000	features/009_dinov2_small.npy	concat(final normalized CLS, mean(final normalized patch tokens)); excludes 0 register token(s)
+10	dinov3_vitl16	dinov3_vitl16_lvd1689m	2048	float32	200000	features/010_dinov3_vitl16.npy	concat(final normalized CLS, mean(final normalized patch tokens)); excludes 4 register token(s)
+11	toklip_l_384	toklip_l	1152	float32	200000	features/011_toklip_l_384.npy	mean of final normalized TokLIP semantic tokens; forward_head is not used
+12	toklip_s_256	toklip_s	1152	float32	200000	features/012_toklip_s_256.npy	mean of final normalized TokLIP semantic tokens; forward_head is not used
+13	uniar_bsq	uniar_bsq	4096	float32	200000	features/013_uniar_bsq.npy	mean of UniAR block 27 tokens after 64-bit BSQ quantize/dequantize, BSQ output projection, and the official 2x2 patch merger
+14	unitok_attn	unitok	1024	float32	200000	features/014_unitok_attn.npy	quantize/dequantize -> post_quant_proj -> token mean -> fc_norm, before UniTok projection
+15	vilau_256	vilau	1024	float32	200000	features/015_vilau_256.npy	mean of the penultimate VILA-U SigLIP encoder-block tokens
+16	eupe_convnext_b	eupe_convnext_b	1024	float32	200000	features/016_eupe_convnext_b.npy	EUPE ConvNeXt final-stage global average pooling after the released final LayerNorm
+17	eupe_vit_b	eupe_vit_b	1536	float32	200000	features/017_eupe_vit_b.npy	concat(final normalized EUPE CLS, mean(final normalized patch tokens)); excludes 4 storage tokens
+18	eupe_vit_s	eupe_vit_s	768	float32	200000	features/018_eupe_vit_s.npy	concat(final normalized EUPE CLS, mean(final normalized patch tokens)); excludes 4 storage tokens
+19	eupe_vit_t	eupe_vit_t	384	float32	200000	features/019_eupe_vit_t.npy	concat(final normalized EUPE CLS, mean(final normalized patch tokens)); excludes 4 storage tokens
+20	ijepa_vith14	ijepa	1280	float32	200000	features/020_ijepa_vith14.npy	spatial mean of the normalized RAEv2 I-JEPA-H/14 K=1 tokenizer latent
+21	mc1_b16_224_2.5b	mc1_b16_224_2.5b	768	float32	200000	features/021_mc1_b16_224_2.5b.npy	final normalized CLS before the MetaCLIP 768-to-512 projection
+22	mc1_b16_224_400m	mc1_b16_224_400m	768	float32	200000	features/022_mc1_b16_224_400m.npy	final normalized CLS before the MetaCLIP 768-to-512 projection
+23	mc1_b32_224_2.5b	mc1_b32_224_2.5b	768	float32	200000	features/023_mc1_b32_224_2.5b.npy	final normalized CLS before the MetaCLIP 768-to-512 projection
+24	mc1_b32_224_400m	mc1_b32_224_400m	768	float32	200000	features/024_mc1_b32_224_400m.npy	final normalized CLS before the MetaCLIP 768-to-512 projection
+25	mc1_g14_224_2.5b	mc1_g14_224_2.5b	1664	float32	200000	features/025_mc1_g14_224_2.5b.npy	final normalized CLS before the MetaCLIP 1664-to-1280 projection
+26	mc1_h14_224_2.5b	mc1_h14_224_2.5b	1280	float32	200000	features/026_mc1_h14_224_2.5b.npy	final normalized CLS before the MetaCLIP 1280-to-1024 projection
+27	mc1_h14_224_v1.2	mc1_h14_224_v1.2	1280	float32	200000	features/027_mc1_h14_224_v1.2.npy	final normalized CLS before the MetaCLIP 1280-to-1024 projection
+28	mc1_l14_224_2.5b	mc1_l14_224_2.5b	1024	float32	200000	features/028_mc1_l14_224_2.5b.npy	final normalized CLS before the MetaCLIP 1024-to-768 projection
+29	mc1_l14_224_400m	mc1_l14_224_400m	1024	float32	200000	features/029_mc1_l14_224_400m.npy	final normalized CLS before the MetaCLIP 1024-to-768 projection
+30	mc2_b16_224	mc2_b16_224	768	float32	200000	features/030_mc2_b16_224.npy	final normalized CLS before the MetaCLIP 2 768-to-512 projection
+31	mc2_b16_384	mc2_b16_384	768	float32	200000	features/031_mc2_b16_384.npy	final normalized CLS before the MetaCLIP 2 768-to-512 projection
+32	mc2_b32_224	mc2_b32_224	768	float32	200000	features/032_mc2_b32_224.npy	final normalized CLS before the MetaCLIP 2 768-to-512 projection
+33	mc2_b32_224_mt5	mc2_b32_224_mt5	768	float32	200000	features/033_mc2_b32_224_mt5.npy	final normalized CLS before the MetaCLIP 2 768-to-512 projection
+34	mc2_b32_384	mc2_b32_384	768	float32	200000	features/034_mc2_b32_384.npy	final normalized CLS before the MetaCLIP 2 768-to-512 projection
+35	mc2_g14_224	mc2_g14_224	1664	float32	200000	features/035_mc2_g14_224.npy	final normalized CLS before the MetaCLIP 2 1664-to-1280 projection
+36	mc2_g14_378	mc2_g14_378	1664	float32	200000	features/036_mc2_g14_378.npy	final normalized CLS before the MetaCLIP 2 1664-to-1280 projection
+37	mc2_h14_378	mc2_h14_378	1280	float32	200000	features/037_mc2_h14_378.npy	final normalized CLS before the MetaCLIP 2 1280-to-1024 projection
+38	mc2_l14_224	mc2_l14_224	1024	float32	200000	features/038_mc2_l14_224.npy	final normalized CLS before the MetaCLIP 2 1024-to-768 projection
+39	mc2_m16_224	mc2_m16_224	512	float32	200000	features/039_mc2_m16_224.npy	final normalized CLS before the MetaCLIP 2 512-to-512 projection
+40	mc2_m16_224_mt5	mc2_m16_224_mt5	512	float32	200000	features/040_mc2_m16_224_mt5.npy	final normalized CLS before the MetaCLIP 2 512-to-512 projection
+41	mc2_m16_384	mc2_m16_384	512	float32	200000	features/041_mc2_m16_384.npy	final normalized CLS before the MetaCLIP 2 512-to-512 projection
+42	mc2_s16_224	mc2_s16_224	384	float32	200000	features/042_mc2_s16_224.npy	final normalized CLS before the MetaCLIP 2 384-to-384 projection
+43	mc2_s16_224_mt5	mc2_s16_224_mt5	384	float32	200000	features/043_mc2_s16_224_mt5.npy	final normalized CLS before the MetaCLIP 2 384-to-384 projection
+44	mc2_s16_384	mc2_s16_384	384	float32	200000	features/044_mc2_s16_384.npy	final normalized CLS before the MetaCLIP 2 384-to-384 projection
+45	pe_core_b16_224	pe_core_b16_224	768	float32	200000	features/045_pe_core_b16_224.npy	PE-Core learned attention-pool output before the released CLIP projection
+46	pe_core_g14_448	pe_core_g14_448	1536	float32	200000	features/046_pe_core_g14_448.npy	PE-Core learned attention-pool output before the released CLIP projection
+47	pe_lang_l14_448	pe_lang_l14_448	1024	float32	200000	features/047_pe_lang_l14_448.npy	mean of last-layer Perception Encoder patch tokens, excluding any CLS token
+48	pixio_vitb16	pixio_vitb16	768	float32	200000	features/048_pixio_vitb16.npy	mean of the eight final-LayerNorm Pixio CLS tokens
+49	pixio_vith16	pixio_vith16	1280	float32	200000	features/049_pixio_vith16.npy	mean of the eight final-LayerNorm Pixio CLS tokens
+50	pixio_vitl16	pixio_vitl16	1024	float32	200000	features/050_pixio_vitl16.npy	mean of the eight final-LayerNorm Pixio CLS tokens
+51	raev2_dinov3l_k7	raev2_dinov3l_k7	1024	float32	200000	features/051_raev2_dinov3l_k7.npy	spatial mean of the normalized RAEv2 DINOv3-L/16 K=7 multi-layer tokenizer latent
+52	siglip2_b16_224	siglip2_b16_224	768	float32	200000	features/052_siglip2_b16_224.npy	native SigLIP 2 model(images) [B,D] output after MAP attention pooling
+53	siglip2_b16_256	siglip2_b16_256	768	float32	200000	features/053_siglip2_b16_256.npy	native SigLIP 2 model(images) [B,D] output after MAP attention pooling
+54	siglip2_b16_384	siglip2_b16_384	768	float32	200000	features/054_siglip2_b16_384.npy	native SigLIP 2 model(images) [B,D] output after MAP attention pooling
+55	siglip2_b16_512	siglip2_b16_512	768	float32	200000	features/055_siglip2_b16_512.npy	native SigLIP 2 model(images) [B,D] output after MAP attention pooling
+56	siglip2_b32_256	siglip2_b32_256	768	float32	200000	features/056_siglip2_b32_256.npy	native SigLIP 2 model(images) [B,D] output after MAP attention pooling
+57	siglip2_g16_256	siglip2_g16_256	1536	float32	200000	features/057_siglip2_g16_256.npy	native SigLIP 2 model(images) [B,D] output after MAP attention pooling
+58	siglip2_g16_384	siglip2_g16_384	1536	float32	200000	features/058_siglip2_g16_384.npy	native SigLIP 2 model(images) [B,D] output after MAP attention pooling
+59	siglip2_l16_256	siglip2_l16_256	1024	float32	200000	features/059_siglip2_l16_256.npy	native SigLIP 2 model(images) [B,D] output after MAP attention pooling
+60	siglip2_l16_384	siglip2_l16_384	1024	float32	200000	features/060_siglip2_l16_384.npy	native SigLIP 2 model(images) [B,D] output after MAP attention pooling
+61	siglip2_l16_512	siglip2_l16_512	1024	float32	200000	features/061_siglip2_l16_512.npy	native SigLIP 2 model(images) [B,D] output after MAP attention pooling
+62	siglip2_sm14_224	siglip2_sm14_224	1152	float32	200000	features/062_siglip2_sm14_224.npy	native SigLIP 2 model(images) [B,D] output after MAP attention pooling
+63	siglip2_sm14_384	siglip2_sm14_384	1152	float32	200000	features/063_siglip2_sm14_384.npy	native SigLIP 2 model(images) [B,D] output after MAP attention pooling
+64	siglip2_sm16_256	siglip2_sm16_256	1152	float32	200000	features/064_siglip2_sm16_256.npy	native SigLIP 2 model(images) [B,D] output after MAP attention pooling
+65	siglip2_sm16_384	siglip2_sm16_384	1152	float32	200000	features/065_siglip2_sm16_384.npy	native SigLIP 2 model(images) [B,D] output after MAP attention pooling
+66	siglip2_sm16_512	siglip2_sm16_512	1152	float32	200000	features/066_siglip2_sm16_512.npy	native SigLIP 2 model(images) [B,D] output after MAP attention pooling
+67	webssl_dino1b_full2b_224	webssl_dino1b_full2b_224	1536	float32	200000	features/067_webssl_dino1b_full2b_224.npy	final normalized WebSSL-DINO encoder CLS token
+68	webssl_mae1b_full2b_224	webssl_mae1b_full2b_224	1536	float32	200000	features/068_webssl_mae1b_full2b_224.npy	final normalized WebSSL-MAE encoder CLS token
+69	webssl_mae300m_full2b_224	webssl_mae300m_full2b_224	1024	float32	200000	features/069_webssl_mae300m_full2b_224.npy	final normalized WebSSL-MAE encoder CLS token
+70	webssl_mae3b_full2b_224	webssl_mae3b_full2b_224	3072	float32	200000	features/070_webssl_mae3b_full2b_224.npy	final normalized WebSSL-MAE encoder CLS token

imagenet1k/train200k/metadata/001_clip_openai__l14.json

@@ -0,0 +1,22 @@
+{
+  "dtype": "float32",
+  "feature_dim": 1024,
+  "feature_file": "features/001_clip_openai__l14.npy",
+  "model": "clip_openai__l14",
+  "rank": 1,
+  "representation": "final post-LayerNorm CLS before the OpenAI CLIP 1024-to-768 visual projection",
+  "row_order": "shared source_indices.npy (strictly increasing official ImageNet train indices)",
+  "rows": 200000,
+  "source_cache_fingerprint": "1936cc8df71414558f44b4bb7f4c4c9bb6771ee04824761229d62870d05035e4",
+  "source_cache_metadata": "source-local:metadata.json",
+  "source_kind": "subset-from-full-train-cache",
+  "tokenizer": "clip_openai__l14",
+  "release": {
+    "array_file": "imagenet1k/train200k/features/001_clip_openai__l14.npy",
+    "original_metadata_sha256": "e40d1385687cc95e318cfa249881972de579f609567fe67647abc31fd57ecdfb",
+    "source_indices_file": "imagenet1k/train200k/source_indices.npy",
+    "labels_file": "imagenet1k/train200k/labels.npy",
+    "row_order_indices_sha256": "200d9a05c37f0e816cfed2d5c1a0bea55550fa0b2a9c79f874f9a86c472ce360",
+    "historical_provenance": "Source cache fingerprint and representation retained; the original upstream cache metadata was unavailable at release preparation."
+  }
+}

imagenet1k/train200k/metadata/002_dino_vitb16.json

@@ -0,0 +1,22 @@
+{
+  "dtype": "float32",
+  "feature_dim": 1536,
+  "feature_file": "features/002_dino_vitb16.npy",
+  "model": "dinov1_vitb16",
+  "rank": 2,
+  "representation": "concat(final normalized CLS, mean(final normalized patch tokens)); excludes 0 register token(s)",
+  "row_order": "shared source_indices.npy (strictly increasing official ImageNet train indices)",
+  "rows": 200000,
+  "source_cache_fingerprint": "f38e17d7409aad286f3ed88205e772e9a9e13e2eafb310bcfe723e853a9ddcf8",
+  "source_cache_metadata": "source-local:metadata.json",
+  "source_kind": "subset-from-full-train-cache",
+  "tokenizer": "dino_vitb16",
+  "release": {
+    "array_file": "imagenet1k/train200k/features/002_dino_vitb16.npy",
+    "original_metadata_sha256": "49363657116203522e2af8d9674ac9d038b40572b21270ac4e33ae9dfbce0006",
+    "source_indices_file": "imagenet1k/train200k/source_indices.npy",
+    "labels_file": "imagenet1k/train200k/labels.npy",
+    "row_order_indices_sha256": "200d9a05c37f0e816cfed2d5c1a0bea55550fa0b2a9c79f874f9a86c472ce360",
+    "historical_provenance": "Source cache fingerprint and representation retained; the original upstream cache metadata was unavailable at release preparation."
+  }
+}

imagenet1k/train200k/metadata/003_dino_vitb8.json

@@ -0,0 +1,22 @@
+{
+  "dtype": "float32",
+  "feature_dim": 1536,
+  "feature_file": "features/003_dino_vitb8.npy",
+  "model": "dinov1_vitb8",
+  "rank": 3,
+  "representation": "concat(final normalized CLS, mean(final normalized patch tokens)); excludes 0 register token(s)",
+  "row_order": "shared source_indices.npy (strictly increasing official ImageNet train indices)",
+  "rows": 200000,
+  "source_cache_fingerprint": "ce9952141bdaa21f42ab027acf24bd77300f73b7796241a070cd643d1a15fd77",
+  "source_cache_metadata": "source-local:metadata.json",
+  "source_kind": "subset-from-full-train-cache",
+  "tokenizer": "dino_vitb8",
+  "release": {
+    "array_file": "imagenet1k/train200k/features/003_dino_vitb8.npy",
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+}

imagenet1k/train200k/metadata/004_dino_vits16.json

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+{
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+}

imagenet1k/train200k/metadata/005_dino_vits8.json

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+{
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+}

imagenet1k/train200k/metadata/006_dinov2_base.json

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+{
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+}

imagenet1k/train200k/metadata/007_dinov2_giant.json

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+{
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+}

imagenet1k/train200k/metadata/008_dinov2_large.json

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+{
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+}

imagenet1k/train200k/metadata/009_dinov2_small.json

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+{
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+}

imagenet1k/train200k/metadata/010_dinov3_vitl16.json

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+{
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+}

imagenet1k/train200k/metadata/011_toklip_l_384.json

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+{
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+}

imagenet1k/train200k/metadata/012_toklip_s_256.json

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+{
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+}

imagenet1k/train200k/metadata/013_uniar_bsq.json

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+{
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+}

imagenet1k/train200k/metadata/014_unitok_attn.json

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+{
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+}

imagenet1k/train200k/metadata/015_vilau_256.json

@@ -0,0 +1,22 @@
+{
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+}

imagenet1k/train200k/metadata/016_eupe_convnext_b.json

@@ -0,0 +1,22 @@
+{
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+}

imagenet1k/train200k/metadata/017_eupe_vit_b.json

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+{
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+}

imagenet1k/train200k/metadata/018_eupe_vit_s.json

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+{
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+}

imagenet1k/train200k/metadata/019_eupe_vit_t.json

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+{
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imagenet1k/train200k/metadata/020_ijepa_vith14.json

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+{
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imagenet1k/train200k/metadata/021_mc1_b16_224_2.5b.json

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+{
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imagenet1k/train200k/metadata/022_mc1_b16_224_400m.json

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+{
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+}

imagenet1k/train200k/metadata/023_mc1_b32_224_2.5b.json

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+{
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+}

imagenet1k/train200k/metadata/024_mc1_b32_224_400m.json

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+{
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imagenet1k/train200k/metadata/025_mc1_g14_224_2.5b.json

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+{
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+}

imagenet1k/train200k/metadata/026_mc1_h14_224_2.5b.json

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+{
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imagenet1k/train200k/metadata/027_mc1_h14_224_v1.2.json

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+{
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imagenet1k/train200k/metadata/028_mc1_l14_224_2.5b.json

@@ -0,0 +1,22 @@
+{
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imagenet1k/train200k/metadata/029_mc1_l14_224_400m.json

@@ -0,0 +1,22 @@
+{
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imagenet1k/train200k/metadata/030_mc2_b16_224.json

@@ -0,0 +1,22 @@
+{
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imagenet1k/train200k/metadata/031_mc2_b16_384.json

@@ -0,0 +1,22 @@
+{
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+}

imagenet1k/train200k/metadata/032_mc2_b32_224.json

@@ -0,0 +1,22 @@
+{
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+}

imagenet1k/train200k/metadata/033_mc2_b32_224_mt5.json

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+{
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imagenet1k/train200k/metadata/034_mc2_b32_384.json

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+{
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imagenet1k/train200k/metadata/035_mc2_g14_224.json

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+{
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imagenet1k/train200k/metadata/036_mc2_g14_378.json

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+{
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imagenet1k/train200k/metadata/037_mc2_h14_378.json

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+{
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+}

imagenet1k/train200k/metadata/038_mc2_l14_224.json

@@ -0,0 +1,22 @@
+{
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imagenet1k/train200k/metadata/039_mc2_m16_224.json

@@ -0,0 +1,22 @@
+{
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imagenet1k/train200k/metadata/040_mc2_m16_224_mt5.json

@@ -0,0 +1,22 @@
+{
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imagenet1k/train200k/metadata/041_mc2_m16_384.json

@@ -0,0 +1,22 @@
+{
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+}

imagenet1k/train200k/metadata/042_mc2_s16_224.json

@@ -0,0 +1,22 @@
+{
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imagenet1k/train200k/metadata/043_mc2_s16_224_mt5.json

@@ -0,0 +1,22 @@
+{
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+}