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"uniar_repaired_array_hash_verified": true + }, + "release_status": "upload_in_progress", + "inventory_note": "FILES.json and checksums.sha256 are excluded from the listed file count/hashes to avoid circular hashes." +} diff --git a/LICENSES.md b/LICENSES.md new file mode 100644 index 0000000000000000000000000000000000000000..59fde878fadfc5d1f92aeac1656a97ad94063b69 --- /dev/null +++ b/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. diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..f811cbe6470c0a29aa8ff30d99f4326a8dd61bc6 --- /dev/null +++ b/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: + - 100KA Strong Baseline for Evaluating Vision Encoders
in Multimodal Large Language Models + +

RAVEL · Vision Encoder Feature Dataset

+ +

Yilin Yang · Jun-Tao Tang · Kengyi Wang
Siyuan Su · Gaoyong Luo · Mingda Chen

+ +

+ Paper + Code + MLLM checkpoints + Feature files + Citation +

+ +## 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/ + _patch_n1000_seed42.npy + _patch_n1000_seed42.json + text/ + _penultimate_mean_n1000_seed42.npy + _penultimate_mean_n1000_seed42.audit.json +imagenet1k/train200k/ + samples/{samples.jsonl,manifest.json} + features/_.npy + metadata/_.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:`. + +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). diff --git a/checksums.sha256 b/checksums.sha256 new file mode 100644 index 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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 diff --git a/imagenet1k/train200k/metadata/001_clip_openai__l14.json b/imagenet1k/train200k/metadata/001_clip_openai__l14.json new file mode 100644 index 0000000000000000000000000000000000000000..1fca35e42de7fa5047e12995fe3a81208ccaf8d6 --- /dev/null +++ b/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": 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0000000000000000000000000000000000000000..e417f5ef6d89c25186e10ff155126849f024831d --- /dev/null +++ b/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." + } +} diff --git a/imagenet1k/train200k/metadata/003_dino_vitb8.json b/imagenet1k/train200k/metadata/003_dino_vitb8.json new file mode 100644 index 0000000000000000000000000000000000000000..d850e74b02db90ea8cdc91e755c3fdd612042b06 --- /dev/null +++ b/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", + "original_metadata_sha256": "14d5297f35f9e3a23fd1e6371d8c897745bf27c9325d715051633975a88e079a", + "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." + } +} diff --git a/imagenet1k/train200k/metadata/004_dino_vits16.json b/imagenet1k/train200k/metadata/004_dino_vits16.json new file mode 100644 index 0000000000000000000000000000000000000000..32a2d035a11e34f8b94c0e7e2c535ef9e74ee973 --- /dev/null +++ b/imagenet1k/train200k/metadata/004_dino_vits16.json @@ -0,0 +1,22 @@ +{ + "dtype": "float32", + "feature_dim": 768, + "feature_file": "features/004_dino_vits16.npy", + "model": "dinov1_vits16", + "rank": 4, + "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": "ecf26abf358b24c8b118f30d34d4da2ac53d346f4f2d979a37b3fa7d6302f0fa", + "source_cache_metadata": "source-local:metadata.json", + "source_kind": "subset-from-full-train-cache", + "tokenizer": "dino_vits16", + "release": { + "array_file": "imagenet1k/train200k/features/004_dino_vits16.npy", + "original_metadata_sha256": "4a9d1d0f3c7a432040fe72690315c06caf90ab7a93f79f6a180097212560d2fb", + "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." + } +} diff --git a/imagenet1k/train200k/metadata/005_dino_vits8.json b/imagenet1k/train200k/metadata/005_dino_vits8.json new file mode 100644 index 0000000000000000000000000000000000000000..acbaf3a3f86715260f778d1b3d97227e1c5d2beb --- /dev/null +++ b/imagenet1k/train200k/metadata/005_dino_vits8.json @@ -0,0 +1,22 @@ +{ + "dtype": "float32", + "feature_dim": 768, + "feature_file": "features/005_dino_vits8.npy", + "model": "dinov1_vits8", + "rank": 5, + "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": "a54d03b13c3f5a6def5710c2817022809441c3d35a8ccccab8b963862a4d7fb6", + "source_cache_metadata": "source-local:metadata.json", + "source_kind": "subset-from-full-train-cache", + "tokenizer": "dino_vits8", + "release": { + "array_file": "imagenet1k/train200k/features/005_dino_vits8.npy", + "original_metadata_sha256": "3e0f44ebaca43e575b7d63d871fcf75fd69dba96abb510b99a27f554a2cb2d40", + "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." + } +} diff --git a/imagenet1k/train200k/metadata/006_dinov2_base.json b/imagenet1k/train200k/metadata/006_dinov2_base.json new file mode 100644 index 0000000000000000000000000000000000000000..a533823e5301ad6712941bfd56a72efa86928a90 --- /dev/null +++ b/imagenet1k/train200k/metadata/006_dinov2_base.json @@ -0,0 +1,22 @@ +{ + "dtype": "float32", + "feature_dim": 1536, + "feature_file": "features/006_dinov2_base.npy", + "model": "dinov2_base", + "rank": 6, + "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": "f4c8e17f59355056628f26b3a3a27d6590a12f48e657159c7a72b43c148315e6", + "source_cache_metadata": "source-local:metadata.json", + "source_kind": "subset-from-full-train-cache", + "tokenizer": "dinov2_base", + "release": { + "array_file": "imagenet1k/train200k/features/006_dinov2_base.npy", + "original_metadata_sha256": "fddafe11d69a54ae8a2f0db5267d4ef13e15f7b37ec13c24490ba359cc25d37a", + "source_indices_file": 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index 0000000000000000000000000000000000000000..e12919df6bfaf0e1477ee7b0205ee178d9613ae8 --- /dev/null +++ b/imagenet1k/train200k/metadata/008_dinov2_large.json @@ -0,0 +1,22 @@ +{ + "dtype": "float32", + "feature_dim": 2048, + "feature_file": "features/008_dinov2_large.npy", + "model": "dinov2_large", + "rank": 8, + "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": "5956fce93b2a516c6dfc3ac5eaf7d0bba20e6c9c73217e4351dacb1a4c64c466", + "source_cache_metadata": "source-local:metadata.json", + "source_kind": "subset-from-full-train-cache", + "tokenizer": "dinov2_large", + "release": { + "array_file": "imagenet1k/train200k/features/008_dinov2_large.npy", + "original_metadata_sha256": "0c2e658205d1db442fab3d86215c6d689ba78dd42851ba56dc5870736aa6717f", + "source_indices_file": 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index 0000000000000000000000000000000000000000..3031d36003f84f613d94f3d516986b9c00df7228 --- /dev/null +++ b/imagenet1k/train200k/metadata/010_dinov3_vitl16.json @@ -0,0 +1,22 @@ +{ + "dtype": "float32", + "feature_dim": 2048, + "feature_file": "features/010_dinov3_vitl16.npy", + "model": "dinov3_vitl16_lvd1689m", + "rank": 10, + "representation": "concat(final normalized CLS, mean(final normalized patch tokens)); excludes 4 register token(s)", + "row_order": "shared source_indices.npy (strictly increasing official ImageNet train indices)", + "rows": 200000, + "source_cache_fingerprint": "710100a4de4dd7a0703feb89f39ccd6ba13aa97e0a23414fe8f12bd854fe8fb9", + "source_cache_metadata": "source-local:metadata.json", + "source_kind": "subset-from-full-train-cache", + "tokenizer": "dinov3_vitl16", + "release": { + "array_file": "imagenet1k/train200k/features/010_dinov3_vitl16.npy", + "original_metadata_sha256": "ede711f10684fa0d2b68e8d42244b09c238023a698d1bc92087ae16b3d93796a", + 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adapter ring for f7", + "source_record": { + "id": "003404984", + "image": "00340/003404984.jpg", + "conversations": [ + { + "from": "human", + "value": "\nGive a short and clear explanation of the subsequent image." + }, + { + "from": "gpt", + "value": "canon adapter ring for f7" + } + ] + }, + "row_index": 498 + }, + { + "index": 551642, + "image_id": "00426/004269977.jpg", + "image_path": "00426/004269977.jpg", + "text_id": "004269977", + "text": "peter dear framed print by paul meijerer", + "source_record": { + "id": "004269977", + "image": "00426/004269977.jpg", + "conversations": [ + { + "from": "human", + "value": "\nWrite a terse but informative summary of the picture." + }, + { + "from": "gpt", + "value": "peter dear framed print by paul meijerer" + } + ] + }, + "row_index": 499 + }, + { + "index": 39174, + "image_id": "00124/001246399.jpg", + "image_path": "00124/001246399.jpg", + "text_id": "001246399", + "text": "maria - e is shown at an event", + "source_record": { + "id": "001246399", + "image": "00124/001246399.jpg", + "conversations": [ + { + "from": "human", + "value": "Give a short and clear explanation of the subsequent image.\n" + }, + { + "from": "gpt", + "value": "maria - e is shown at an event" + } + ] + }, + "row_index": 500 + }, + { + "index": 374175, + "image_id": "00095/000951289.jpg", + "image_path": "00095/000951289.jpg", + "text_id": "000951289", + "text": "american idol logo", + "source_record": { + "id": "000951289", + "image": "00095/000951289.jpg", + "conversations": [ + { + "from": "human", + "value": "\nProvide a brief description of the given image." + }, + { + "from": "gpt", + "value": "american idol logo" + } + ] + }, + "row_index": 501 + }, + { + "index": 204726, + "image_id": "00079/000790333.jpg", + "image_path": "00079/000790333.jpg", + "text_id": "000790333", + "text": "the poster for breaking benjamin with the skullet around and the hyperax", + "source_record": { + "id": "000790333", + "image": "00079/000790333.jpg", + "conversations": [ + { + "from": "human", + "value": "Describe the image concisely.\n" + }, + { + "from": "gpt", + "value": "the poster for breaking benjamin with the skullet around and the hyperax" + } + ] + }, + "row_index": 502 + }, + { + "index": 436301, + "image_id": "00227/002279856.jpg", + "image_path": "00227/002279856.jpg", + "text_id": "002279856", + "text": "a magnifying glass with the word fact under it", + "source_record": { + "id": "002279856", + "image": "00227/002279856.jpg", + "conversations": [ + { + "from": "human", + "value": "Summarize the visual content of the image.\n" + }, + { + "from": "gpt", + "value": "a magnifying glass with the word fact under it" + } + ] + }, + "row_index": 503 + }, + { + "index": 199560, + "image_id": "00609/006099255.jpg", + "image_path": "00609/006099255.jpg", + "text_id": "006099255", + "text": "a woman with a cow hat is posing at the fence", + "source_record": { + "id": "006099255", + "image": "00609/006099255.jpg", + "conversations": [ + { + "from": "human", + "value": "Render a clear and concise summary of the photo.\n" + }, + { + "from": "gpt", + "value": "a woman with a cow hat is posing at the fence" + } + ] + }, + "row_index": 504 + }, + { + "index": 8788, + "image_id": "00390/003902554.jpg", + "image_path": "00390/003902554.jpg", + "text_id": "003902554", + "text": "a close - up of the front brake in the bikepack", + "source_record": { + "id": "003902554", + "image": "00390/003902554.jpg", + "conversations": [ + { + "from": "human", + "value": "\nRender a clear and concise summary of the photo." + }, + { + "from": "gpt", + "value": "a close - up of the front brake in the bikepack" + } + ] + }, + "row_index": 505 + }, + { + "index": 280740, + "image_id": "00066/000662132.jpg", + "image_path": "00066/000662132.jpg", + "text_id": "000662132", + "text": "jamie evans arrives at the launch for the calvin klein fragrance in sydney", + "source_record": { + "id": "000662132", + "image": "00066/000662132.jpg", + "conversations": [ + { + "from": "human", + "value": "Give a brief description of the image.\n" + }, + { + "from": "gpt", + "value": "jamie evans arrives at the launch for the calvin klein fragrance in sydney" + } + ] + }, + "row_index": 506 + }, + { + "index": 468860, + "image_id": "00271/002719056.jpg", + "image_path": "00271/002719056.jpg", + "text_id": "002719056", + "text": "the image shows a pink inky paint blot on a white background", + "source_record": { + "id": "002719056", + "image": "00271/002719056.jpg", + "conversations": [ + { + "from": "human", + "value": "Render a clear and concise summary of the photo.\n" + }, + { + "from": "gpt", + "value": "the image shows a pink inky paint blot on a white background" + } + ] + }, + "row_index": 507 + }, + { + "index": 23194, + "image_id": "00292/002928750.jpg", + "image_path": "00292/002928750.jpg", + "text_id": "002928750", + "text": "a 17th century oil on canvas portrait of a gentleman", + "source_record": { + "id": "002928750", + "image": "00292/002928750.jpg", + "conversations": [ + { + "from": "human", + "value": "Present a compact description of the photo's key features.\n" + }, + { + "from": "gpt", + "value": "a 17th century oil on canvas portrait of a gentleman" + } + ] + }, + "row_index": 508 + }, + { + "index": 547649, + "image_id": "00035/000359870.jpg", + "image_path": "00035/000359870.jpg", + "text_id": "000359870", + "text": "a woman smoking a cigarette and making a smoke sign", + "source_record": { + "id": "000359870", + "image": "00035/000359870.jpg", + "conversations": [ + { + "from": "human", + "value": "Summarize the visual content of the image.\n" + }, + { + "from": "gpt", + "value": "a woman smoking a cigarette and making a smoke sign" + } + ] + }, + "row_index": 509 + }, + { + "index": 29589, + "image_id": "00062/000624634.jpg", + "image_path": "00062/000624634.jpg", + "text_id": "000624634", + "text": "the radwood films logo", + "source_record": { + "id": "000624634", + "image": "00062/000624634.jpg", + "conversations": [ + { + "from": "human", + "value": "Give a brief description of the image.\n" + }, + { + "from": "gpt", + "value": "the radwood films logo" + } + ] + }, + "row_index": 510 + }, + { + "index": 56703, + "image_id": "00547/005475271.jpg", + "image_path": "00547/005475271.jpg", + "text_id": "005475271", + "text": "a shower bench with a soap dispenser and sponge on it", + "source_record": { + "id": "005475271", + "image": "00547/005475271.jpg", + "conversations": [ + { + "from": "human", + "value": "\nWhat is this?" + }, + { + "from": "gpt", + "value": "a shower bench with a soap dispenser and sponge on it" + } + ] + }, + "row_index": 511 + }, + { + "index": 256853, + "image_id": "00103/001034442.jpg", + "image_path": "00103/001034442.jpg", + "text_id": "001034442", + "text": "female hands with red nail polish stock photo © yapi", + "source_record": { + "id": "001034442", + "image": "00103/001034442.jpg", + "conversations": [ + { + "from": "human", + "value": "Present a compact description of the photo's key features.\n" + }, + { + "from": "gpt", + "value": "female hands with red nail polish stock photo © yapi" + } + ] + }, + "row_index": 512 + }, + { + "index": 400439, + "image_id": "00036/000364534.jpg", + "image_path": "00036/000364534.jpg", + "text_id": "000364534", + "text": "colorful rainbow color number seven logo design icon element royalty illustration", + "source_record": { + "id": "000364534", + "image": "00036/000364534.jpg", + "conversations": [ + { + "from": "human", + "value": "\nSummarize the visual content of the image." + }, + { + "from": "gpt", + "value": "colorful rainbow color number seven logo design icon element royalty illustration" + } + ] + }, + "row_index": 513 + }, + { + "index": 451030, + "image_id": "00528/005287070.jpg", + "image_path": "00528/005287070.jpg", + "text_id": "005287070", + "text": "a combine harvesting wheat", + "source_record": { + "id": "005287070", + "image": "00528/005287070.jpg", + "conversations": [ + { + "from": "human", + "value": "Describe the image concisely.\n" + }, + { + "from": "gpt", + "value": "a combine harvesting wheat" + } + ] + }, + "row_index": 514 + }, + { + "index": 125904, + "image_id": "00355/003550431.jpg", + "image_path": "00355/003550431.jpg", + "text_id": "003550431", + "text": "jesus art print by michael g westmeyer", + "source_record": { + "id": "003550431", + "image": "00355/003550431.jpg", + "conversations": [ + { + "from": "human", + "value": "What is in the photo?\n" + }, + { + "from": "gpt", + "value": "jesus art print by michael g westmeyer" + } + ] + }, + "row_index": 515 + }, + { + "index": 78065, + "image_id": "00110/001105779.jpg", + "image_path": "00110/001105779.jpg", + "text_id": "001105779", + "text": "a grandpa is the perfect teacher t - shirt", + "source_record": { + "id": "001105779", + "image": "00110/001105779.jpg", + "conversations": [ + { + "from": "human", + "value": "\nPresent a compact description of the photo's key features." + }, + { + "from": "gpt", + "value": "a grandpa is the perfect teacher t - shirt" + } + ] + }, + "row_index": 516 + }, + { + "index": 476847, + "image_id": "00067/000674413.jpg", + "image_path": "00067/000674413.jpg", + "text_id": "000674413", + "text": "two rats in a cage with text saying face transplants the early test subjects", + "source_record": { + "id": "000674413", + "image": "00067/000674413.jpg", + "conversations": [ + { + "from": "human", + "value": "Write a terse but informative summary of the picture.\n" + }, + { + "from": "gpt", + "value": "two rats in a cage with text saying face transplants the early test subjects" + } + ] + }, + "row_index": 517 + }, + { + "index": 503427, + "image_id": "00312/003123060.jpg", + "image_path": "00312/003123060.jpg", + "text_id": "003123060", + "text": "sample resume for an it technician and a technical", + "source_record": { + "id": "003123060", + "image": "00312/003123060.jpg", + "conversations": [ + { + "from": "human", + "value": "\nShare a concise interpretation of the image provided." + }, + { + "from": "gpt", + "value": "sample resume for an it technician and a technical" + } + ] + }, + "row_index": 518 + }, + { + "index": 80990, + "image_id": "00216/002163334.jpg", + "image_path": "00216/002163334.jpg", + "text_id": "002163334", + "text": "the limited collection sweaters", + "source_record": { + "id": "002163334", + "image": "00216/002163334.jpg", + "conversations": [ + { + "from": "human", + "value": "Give a brief description of the image.\n" + }, + { + "from": "gpt", + "value": "the limited collection sweaters" + } + ] + }, + "row_index": 519 + }, + { + "index": 53723, + "image_id": "00291/002912916.jpg", + "image_path": "00291/002912916.jpg", + "text_id": "002912916", + "text": "campbell's abstract of greek indians, census cards and index", + "source_record": { + "id": "002912916", + "image": "00291/002912916.jpg", + "conversations": [ + { + "from": "human", + "value": "Summarize the visual content of the image.\n" + }, + { + "from": "gpt", + "value": "campbell's abstract of greek indians, census cards and index" + } + ] + }, + "row_index": 520 + }, + { + "index": 244974, + "image_id": "00407/004077056.jpg", + "image_path": "00407/004077056.jpg", + "text_id": "004077056", + "text": "an ivory carpet with white dots in a pattern on a blue ground", + "source_record": { + "id": "004077056", + "image": "00407/004077056.jpg", + "conversations": [ + { + "from": "human", + "value": "Describe the image concisely.\n" + }, + { + "from": "gpt", + "value": "an ivory carpet with white dots in a pattern on a blue ground" + } + ] + }, + "row_index": 521 + }, + { + "index": 102418, + "image_id": "00123/001234669.jpg", + "image_path": "00123/001234669.jpg", + "text_id": "001234669", + "text": "marketing communications strategy", + "source_record": { + "id": "001234669", + "image": "00123/001234669.jpg", + "conversations": [ + { + "from": "human", + "value": "\nPresent a compact description of the photo's key features." + }, + { + "from": "gpt", + "value": "marketing communications strategy" + } + ] + }, + "row_index": 522 + }, + { + "index": 57635, + "image_id": "00170/001709584.jpg", + "image_path": "00170/001709584.jpg", + "text_id": "001709584", + "text": "maisonmains floral pattern jumper white", + "source_record": { + "id": "001709584", + "image": "00170/001709584.jpg", + "conversations": [ + { + "from": "human", + "value": "\nProvide a brief description of the given image." + }, + { + "from": "gpt", + "value": "maisonmains floral pattern jumper white" + } + ] + }, + "row_index": 523 + }, + { + "index": 281438, + "image_id": "00075/000759546.jpg", + "image_path": "00075/000759546.jpg", + "text_id": "000759546", + "text": "a map of the town of allston on an old sheet", + "source_record": { + "id": "000759546", + "image": "00075/000759546.jpg", + "conversations": [ + { + "from": "human", + "value": "\nDescribe the image concisely." + }, + { + "from": "gpt", + "value": "a map of the town of allston on an old sheet" + } + ] + }, + "row_index": 524 + }, + { + "index": 493381, + "image_id": "00132/001325507.jpg", + "image_path": "00132/001325507.jpg", + "text_id": "001325507", + "text": "solar garden spotlight light projector lighting for outdoor yard yard", + "source_record": { + "id": "001325507", + "image": "00132/001325507.jpg", + "conversations": [ + { + "from": "human", + "value": "Summarize the visual content of the image.\n" + }, + { + "from": "gpt", + "value": "solar garden spotlight light projector lighting for outdoor yard yard" + } + ] + }, + "row_index": 525 + }, + { + "index": 392944, + "image_id": "00091/000910281.jpg", + "image_path": "00091/000910281.jpg", + "text_id": "000910281", + "text": "a simple gray butterfly with white stripes necklace", + "source_record": { + "id": "000910281", + "image": "00091/000910281.jpg", + "conversations": [ + { + "from": "human", + "value": "Give a short and clear explanation of the subsequent image.\n" + }, + { + "from": "gpt", + "value": "a simple gray butterfly with white stripes necklace" + } + ] + }, + "row_index": 526 + }, + { + "index": 356712, + "image_id": "00604/006049695.jpg", + "image_path": "00604/006049695.jpg", + "text_id": "006049695", + "text": "a purple and white flyer with information for school lunch hours", + "source_record": { + "id": "006049695", + "image": "00604/006049695.jpg", + "conversations": [ + { + "from": "human", + "value": "\nPresent a compact description of the photo's key features." + }, + { + "from": "gpt", + "value": "a purple and white flyer with information for school lunch hours" + } + ] + }, + "row_index": 527 + }, + { + "index": 430395, + "image_id": "00032/000329936.jpg", + "image_path": "00032/000329936.jpg", + "text_id": "000329936", + "text": "the tudor houses at these tudor houses have been built, while the style and beauty are still in their place", + "source_record": { + "id": "000329936", + "image": "00032/000329936.jpg", + "conversations": [ + { + "from": "human", + "value": "\nDescribe the image concisely." + }, + { + "from": "gpt", + "value": "the tudor houses at these tudor houses have been built, while the style and beauty are still in their place" + } + ] + }, + "row_index": 528 + }, + { + "index": 136640, + "image_id": "00048/000481431.jpg", + "image_path": "00048/000481431.jpg", + "text_id": "000481431", + "text": "copper pipe, copper fittings, and copper connectors", + "source_record": { + "id": "000481431", + "image": "00048/000481431.jpg", + "conversations": [ + { + "from": "human", + "value": "Write a terse but informative summary of the picture.\n" + }, + { + "from": "gpt", + "value": "copper pipe, copper fittings, and copper connectors" + } + ] + }, + "row_index": 529 + }, + { + "index": 51334, + "image_id": "00020/000202965.jpg", + "image_path": "00020/000202965.jpg", + "text_id": "000202965", + "text": "a collage of students and staff members of the school", + "source_record": { + "id": "000202965", + "image": "00020/000202965.jpg", + "conversations": [ + { + "from": "human", + "value": "Write a terse but informative summary of the picture.\n" + }, + { + "from": "gpt", + "value": "a collage of students and staff members of the school" + } + ] + }, + "row_index": 530 + }, + { + "index": 73496, + "image_id": "00015/000158267.jpg", + "image_path": "00015/000158267.jpg", + "text_id": "000158267", + "text": "a black leather chelsea boot with a brown sole", + "source_record": { + "id": "000158267", + "image": "00015/000158267.jpg", + "conversations": [ + { + "from": "human", + "value": "Present a compact description of the photo's key features.\n" + }, + { + "from": "gpt", + "value": "a black leather chelsea boot with a brown sole" + } + ] + }, + "row_index": 531 + }, + { + "index": 94491, + "image_id": "00202/002021567.jpg", + "image_path": "00202/002021567.jpg", + "text_id": "002021567", + "text": "a man looks out into the distance while standing in front of a black background with a red netflix sign in", + "source_record": { + "id": "002021567", + "image": "00202/002021567.jpg", + "conversations": [ + { + "from": "human", + "value": "Give a brief description of the image.\n" + }, + { + "from": "gpt", + "value": "a man looks out into the distance while standing in front of a black background with a red netflix sign in" + } + ] + }, + "row_index": 532 + }, + { + "index": 531210, + "image_id": "00132/001326075.jpg", + "image_path": "00132/001326075.jpg", + "text_id": "001326075", + "text": "straight love ebook cover", + "source_record": { + "id": "001326075", + "image": "00132/001326075.jpg", + "conversations": [ + { + "from": "human", + "value": "\nDescribe the image concisely." + }, + { + "from": "gpt", + "value": "straight love ebook cover" + } + ] + }, + "row_index": 533 + }, + { + "index": 380586, + "image_id": "00192/001923019.jpg", + "image_path": "00192/001923019.jpg", + "text_id": "001923019", + "text": "the summer camp flyer shows a 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+ "index": 209970, + "image_id": "00004/000046811.jpg", + "image_path": "00004/000046811.jpg", + "text_id": "000046811", + "text": "a piece of wood with a cheese knife and some green olives", + "source_record": { + "id": "000046811", + "image": "00004/000046811.jpg", + "conversations": [ + { + "from": "human", + "value": "Summarize the visual content of the image.\n" + }, + { + "from": "gpt", + "value": "a piece of wood with a cheese knife and some green olives" + } + ] + }, + "row_index": 558 + }, + { + "index": 316296, + "image_id": "00088/000889655.jpg", + "image_path": "00088/000889655.jpg", + "text_id": "000889655", + "text": "ladies full finger riding gloves", + "source_record": { + "id": "000889655", + "image": "00088/000889655.jpg", + "conversations": [ + { + "from": "human", + "value": "\nSummarize the visual content of the image." + }, + { + "from": "gpt", + "value": "ladies full finger riding gloves" + } + ] + }, + "row_index": 559 + }, + { + "index": 85664, + "image_id": 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563 + }, + { + "index": 354883, + "image_id": "00095/000956421.jpg", + "image_path": "00095/000956421.jpg", + "text_id": "000956421", + "text": "two beautiful women having fun in the swimming pool photo", + "source_record": { + "id": "000956421", + "image": "00095/000956421.jpg", + "conversations": [ + { + "from": "human", + "value": "Summarize the visual content of the image.\n" + }, + { + "from": "gpt", + "value": "two beautiful women having fun in the swimming pool photo" + } + ] + }, + "row_index": 564 + }, + { + "index": 522784, + "image_id": "00515/005155836.jpg", + "image_path": "00515/005155836.jpg", + "text_id": "005155836", + "text": "pet bed sofa covers", + "source_record": { + "id": "005155836", + "image": "00515/005155836.jpg", + "conversations": [ + { + "from": "human", + "value": "Share a concise interpretation of the image provided.\n" + }, + { + "from": "gpt", + "value": "pet bed sofa covers" + } + ] + }, + "row_index": 565 + }, + { + "index": 523309, + "image_id": "00253/002532339.jpg", + "image_path": "00253/002532339.jpg", + "text_id": "002532339", + "text": "assassin creed gameplay", + "source_record": { + "id": "002532339", + "image": "00253/002532339.jpg", + "conversations": [ + { + "from": "human", + "value": "\nProvide a brief description of the given image." + }, + { + "from": "gpt", + "value": "assassin creed gameplay" + } + ] + }, + "row_index": 566 + }, + { + "index": 380304, + "image_id": "00229/002299993.jpg", + "image_path": "00229/002299993.jpg", + "text_id": "002299993", + "text": "a dead end warning sign mug", + "source_record": { + "id": "002299993", + "image": "00229/002299993.jpg", + "conversations": [ + { + "from": "human", + "value": "\nDescribe the image concisely." + }, + { + "from": "gpt", + "value": "a dead end warning sign mug" + } + ] + }, + "row_index": 567 + }, + { + "index": 447309, + "image_id": "00262/002620199.jpg", + "image_path": "00262/002620199.jpg", + "text_id": "002620199", + "text": "students playing 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"id": "004914127", + "image": "00491/004914127.jpg", + "conversations": [ + { + "from": "human", + "value": "\nShare a concise interpretation of the image provided." + }, + { + "from": "gpt", + "value": "the tube has an aluminum shaft on both sides" + } + ] + }, + "row_index": 570 + }, + { + "index": 511390, + "image_id": "00454/004542407.jpg", + "image_path": "00454/004542407.jpg", + "text_id": "004542407", + "text": "happy dhanteras, happy dhanteras, dhanteras images, happy dhanteras images", + "source_record": { + "id": "004542407", + "image": "00454/004542407.jpg", + "conversations": [ + { + "from": "human", + "value": "\nGive a brief description of the image." + }, + { + "from": "gpt", + "value": "happy dhanteras, happy dhanteras, dhanteras images, happy dhanteras images" + } + ] + }, + "row_index": 571 + }, + { + "index": 283695, + "image_id": "00108/001083222.jpg", + "image_path": "00108/001083222.jpg", + "text_id": "001083222", + "text": "an old fashioned thanksgiving 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"conversations": [ + { + "from": "human", + "value": "Describe the image concisely.\n" + }, + { + "from": "gpt", + "value": "a dirty cement sidewalk with weeds and leaves scattered around" + } + ] + }, + "row_index": 832 + }, + { + "index": 467863, + "image_id": "00486/004862200.jpg", + "image_path": "00486/004862200.jpg", + "text_id": "004862200", + "text": "woman in red sweater, grey tulle skirt and black pumps", + "source_record": { + "id": "004862200", + "image": "00486/004862200.jpg", + "conversations": [ + { + "from": "human", + "value": "\nGive a brief description of the image." + }, + { + "from": "gpt", + "value": "woman in red sweater, grey tulle skirt and black pumps" + } + ] + }, + "row_index": 833 + }, + { + "index": 174055, + "image_id": "00261/002610198.jpg", + "image_path": "00261/002610198.jpg", + "text_id": "002610198", + "text": "newborn kids girl boys cute soft cotton unicorn tights leggings", + "source_record": { + "id": "002610198", + "image": 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"00322/003221018.jpg", + "conversations": [ + { + "from": "human", + "value": "Give a brief description of the image.\n" + }, + { + "from": "gpt", + "value": "a sandy beach at sunset by richard sherman" + } + ] + }, + "row_index": 836 + }, + { + "index": 378926, + "image_id": "00266/002660042.jpg", + "image_path": "00266/002660042.jpg", + "text_id": "002660042", + "text": "stainless steel pedestal mounted base with flat square corners", + "source_record": { + "id": "002660042", + "image": "00266/002660042.jpg", + "conversations": [ + { + "from": "human", + "value": "What is in the photo?\n" + }, + { + "from": "gpt", + "value": "stainless steel pedestal mounted base with flat square corners" + } + ] + }, + "row_index": 837 + }, + { + "index": 165332, + "image_id": "00335/003351766.jpg", + "image_path": "00335/003351766.jpg", + "text_id": "003351766", + "text": "a view of the maine side lighthouse with a flower garden and blue skies in the background poster by panora", + "source_record": { + "id": "003351766", + "image": "00335/003351766.jpg", + "conversations": [ + { + "from": "human", + "value": "\nWhat is in the photo?" + }, + { + "from": "gpt", + "value": "a view of the maine side lighthouse with a flower garden and blue skies in the background poster by panora" + } + ] + }, + "row_index": 838 + }, + { + "index": 337925, + "image_id": "00087/000870492.jpg", + "image_path": "00087/000870492.jpg", + "text_id": "000870492", + "text": "an led torchlight", + "source_record": { + "id": "000870492", + "image": "00087/000870492.jpg", + "conversations": [ + { + "from": "human", + "value": "Render a clear and concise summary of the photo.\n" + }, + { + "from": "gpt", + "value": "an led torchlight" + } + ] + }, + "row_index": 839 + }, + { + "index": 89754, + "image_id": "00118/001188135.jpg", + "image_path": "00118/001188135.jpg", + "text_id": "001188135", + "text": "led lighting led light recess spotlight for recess lights", + "source_record": { + "id": "001188135", + "image": "00118/001188135.jpg", + "conversations": [ + { + "from": "human", + "value": "Render a clear and concise summary of the photo.\n" + }, + { + "from": "gpt", + "value": "led lighting led light recess spotlight for recess lights" + } + ] + }, + "row_index": 840 + }, + { + "index": 510632, + "image_id": "00096/000964347.jpg", + "image_path": "00096/000964347.jpg", + "text_id": "000964347", + "text": "barbie safari explorer doll with animal playset", + "source_record": { + "id": "000964347", + "image": "00096/000964347.jpg", + "conversations": [ + { + "from": "human", + "value": "Write a terse but informative summary of the picture.\n" + }, + { + "from": "gpt", + "value": "barbie safari explorer doll with animal playset" + } + ] + }, + "row_index": 841 + }, + { + "index": 529709, + "image_id": "00159/001598201.jpg", + "image_path": "00159/001598201.jpg", + "text_id": "001598201", + "text": "a cvt template for creating a resume, using the link in a list", + "source_record": { + "id": "001598201", + "image": "00159/001598201.jpg", + "conversations": [ + { + "from": "human", + "value": "Describe the image concisely.\n" + }, + { + "from": "gpt", + "value": "a cvt template for creating a resume, using the link in a list" + } + ] + }, + "row_index": 842 + }, + { + "index": 111394, + "image_id": "00037/000373592.jpg", + "image_path": "00037/000373592.jpg", + "text_id": "000373592", + "text": "an animated coronavirus icon", + "source_record": { + "id": "000373592", + "image": "00037/000373592.jpg", + "conversations": [ + { + "from": "human", + "value": "Provide a brief description of the given image.\n" + }, + { + "from": "gpt", + "value": "an animated coronavirus icon" + } + ] + }, + "row_index": 843 + }, + { + "index": 66298, + "image_id": "00268/002686183.jpg", + "image_path": "00268/002686183.jpg", + "text_id": "002686183", + "text": "carbone is seen on a display at the company's headquarters in tokyo november 25, 2013 reuters /", + "source_record": { + "id": 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"image_path": "00225/002256659.jpg", + "text_id": "002256659", + "text": "red dice pattern on pink ipad mini sleeve", + "source_record": { + "id": "002256659", + "image": "00225/002256659.jpg", + "conversations": [ + { + "from": "human", + "value": "What is this?\n" + }, + { + "from": "gpt", + "value": "red dice pattern on pink ipad mini sleeve" + } + ] + }, + "row_index": 846 + }, + { + "index": 289260, + "image_id": "00237/002377825.jpg", + "image_path": "00237/002377825.jpg", + "text_id": "002377825", + "text": "four synchronized swimming swimmers in blue swim suits and black tights", + "source_record": { + "id": "002377825", + "image": "00237/002377825.jpg", + "conversations": [ + { + "from": "human", + "value": "\nSummarize the visual content of the image." + }, + { + "from": "gpt", + "value": "four synchronized swimming swimmers in blue swim suits and black tights" + } + ] + }, + "row_index": 847 + }, + { + "index": 13995, + "image_id": "00010/000105234.jpg", + "image_path": "00010/000105234.jpg", + "text_id": "000105234", + "text": "an icon of a cup with wifi connections and the word'what's the problem? 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+ }, + "row_index": 850 + }, + { + "index": 55276, + "image_id": "00128/001288869.jpg", + "image_path": "00128/001288869.jpg", + "text_id": "001288869", + "text": "pokemon pikachu shaped foil balloon", + "source_record": { + "id": "001288869", + "image": "00128/001288869.jpg", + "conversations": [ + { + "from": "human", + "value": "\nGive a short and clear explanation of the subsequent image." + }, + { + "from": "gpt", + "value": "pokemon pikachu shaped foil balloon" + } + ] + }, + "row_index": 851 + }, + { + "index": 297143, + "image_id": "00119/001194211.jpg", + "image_path": "00119/001194211.jpg", + "text_id": "001194211", + "text": "an elephant with a red background galaxy s5 case by leeta b", + "source_record": { + "id": "001194211", + "image": "00119/001194211.jpg", + "conversations": [ + { + "from": "human", + "value": "Present a compact description of the photo's key features.\n" + }, + { + "from": "gpt", + "value": "an elephant with a red background galaxy s5 case by leeta b" 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