Update README.md

README.md

@@ -81,48 +81,11 @@ Each subset includes features for all 70 vision encoders. The LCS subsets also i
 
 Keep the shared sample row order when loading features. Shapes, extraction settings, and sampling details are recorded in the per-file metadata and sample manifests.
 
-```text
-features/
-  lcs-1k/{vision,text,samples}/
-  lcs-10k/{vision,text,samples}/
-  imagenet-200k/{features,metadata,samples,protocols}/
-  encoders.json
-  FILES.json
-  checksums.sha256
-```
-
-## Download
-
-Example: download the CLIP ViT-L/14 image features and matching Qwen3 text features from `lcs-1k` into `features/`.
-
-```python
-from huggingface_hub import snapshot_download
-import numpy as np
-
-root = snapshot_download(
-    repo_id="336labs/VisionEncoder-Eval-ReproData",
-    repo_type="dataset",
-    local_dir="features",
-    allow_patterns=[
-        "encoders.json",
-        "lcs-1k/vision/clip_openai__l14*",
-        "lcs-1k/text/qwen3*",
-        "lcs-1k/samples/**",
-    ],
-)
-vision = np.load(f"{root}/lcs-1k/vision/clip_openai__l14_patch_n1000_seed42.npy", mmap_mode="r")
-text = np.load(f"{root}/lcs-1k/text/qwen3_penultimate_mean_n1000_seed42.npy", mmap_mode="r")
-```
-
-Use the paths in the encoder catalog below to select other features. The HF Dataset Viewer displays sample metadata; the feature arrays are separate `.npy` files. Raw images are available from the linked source datasets.
 
 ## Encoder Catalog
 
 [encoders.json](encoders.json) lists feature paths, shapes, metadata, and upstream encoder sources for all 70 encoders.
 
-<details>
-<summary>Browse all encoders and feature download links</summary>
-
 | Tokenizer ID | Family | LCS `[T, D]` | ImageNet `D` | Feature files |
 | --- | --- | --- | ---: | --- |
 | [`toklip_l_384`](https://huggingface.co/TencentARC/TokLIP) | DISCRETE | `[576, 1152]` | 1152 | [patches](lcs-1k/vision/toklip_l_384_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/011_toklip_l_384.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Eval-ReproData/tree/main/lcs-10k/vision/toklip_l_384) |
@@ -196,18 +159,14 @@ Use the paths in the encoder catalog below to select other features. The HF Data
 | [`webssl_mae300m_full2b_224`](https://huggingface.co/facebook/webssl-mae300m-full2b-224) | SSL | `[196, 1024]` | 1024 | [patches](lcs-1k/vision/webssl_mae300m_full2b_224_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/069_webssl_mae300m_full2b_224.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Eval-ReproData/tree/main/lcs-10k/vision/webssl_mae300m_full2b_224) |
 | [`webssl_mae3b_full2b_224`](https://huggingface.co/facebook/webssl-mae3b-full2b-224) | SSL | `[256, 3072]` | 3072 | [patches](lcs-1k/vision/webssl_mae3b_full2b_224_patch_n1000_seed42.npy) · [pooled](imagenet-200k/features/070_webssl_mae3b_full2b_224.npy) · [10k blocks](https://huggingface.co/datasets/336labs/VisionEncoder-Eval-ReproData/tree/main/lcs-10k/vision/webssl_mae3b_full2b_224) |
 
-</details>
 
 <a id="datasets-required-by-the-evaluation-pipelines"></a>
 
 ## Data Sources
 
-<details>
-<summary>MLLM training, evaluation benchmarks, and baseline datasets</summary>
-
 This repository provides the three cached feature subsets listed below. Raw images, MLLM training data, evaluation benchmarks, and other baseline datasets are obtained separately.
 
-### Data used by each method
+### Data used by each evaluation methods
 
 | Method | Data needed | Available here / source |
 |---|---|---|
@@ -223,32 +182,7 @@ This repository provides the three cached feature subsets listed below. Raw imag
 
 ImageNet kNN features have class labels but no paired captions, so they cannot supply an alignment probe's image–text inputs on their own. A full linear-probe benchmark also needs independent validation data.
 
-### Raw data folders
-
-The following are example local folders. Keep the image subdirectories recorded in each dataset's annotations.
-
-```text
-data/
-  instructions/pretrain/blip_laion_cc_sbu_558k.json
-  instructions/finetune/llava_v1_5_mix665k_drop_ge8kchars.json
-  instructions/test/<dataset>.tsv
-  images/pretrain/
-  images/finetune/
-  images/test/
-  imagenet/{train,val}/
-  coco/
-  cc3m/
-  SPair-71k/
-  tokbench/
-```
-
-**MLLM pretraining:** obtain the JSON and `images.zip` from [LLaVA-Pretrain](https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain). Image names in the JSON are relative to `data/images/pretrain/`.
-
-**MLLM finetuning:** obtain [mix665k](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/blob/main/llava_v1_5_mix665k.json) and its COCO, GQA, OCR-VQA, TextVQA, and Visual Genome images using the [LLaVA data preparation instructions](https://github.com/haotian-liu/LLaVA#visual-instruction-tuning). The project uses `llava_v1_5_mix665k_drop_ge8kchars.json`, which excludes 395 examples with at least 8,000 conversation-text characters. Image names in this JSON are relative to `data/images/finetune/`.
-
-### Downstream MLLM benchmarks
-
-Place the prepared benchmark TSVs in `data/instructions/test/`. Obtain the matching data from these sources:
+### Downstream MLLM benchmarks used in this paper
 
 | Benchmark / required TSV | Source |
 |---|---|
@@ -264,12 +198,8 @@ Place the prepared benchmark TSVs in `data/instructions/test/`. Obtain the match
 | `MSCOCO_KARPATHY_TEST.tsv` | [Karpathy splits](https://cs.stanford.edu/people/karpathy/deepimagesent/) and [COCO images](https://cocodataset.org/#download) |
 | `FLICKR30K_KARPATHY_TEST.tsv` | [Karpathy splits](https://cs.stanford.edu/people/karpathy/deepimagesent/) and [Flickr30k images](https://shannon.cs.illinois.edu/DenotationGraph/) |
 
-Prepare images referenced by the TSVs under `data/images/test/`. Folder names follow the benchmark loader: for example, MMBench v1.1 uses `MMBench_V11/`. TSVs with embedded images can be decoded by the loader. GQA also accepts the filename `GQA_TESTDEV_BALANCED.tsv`.
-
 Use the [VLMEvalKit dataset loaders](https://github.com/open-compass/VLMEvalKit/tree/main/vlmeval/dataset) for benchmark preparation. Original dataset downloads must be converted to the required TSV format; changing a filename alone is insufficient. Configuration and experiment commands are in the code repository's [Setup](https://github.com/JuntaoTang/MLLM-VisionEncoder-Eval#setup) and [Running](https://github.com/JuntaoTang/MLLM-VisionEncoder-Eval#running) sections.
 
-</details>
-
 ## Metadata and Integrity
 
 The released arrays retain their original storage precision. Per-file metadata and sample manifests record available extraction and sampling information. [FILES.json](FILES.json) and [checksums.sha256](checksums.sha256) provide file sizes and SHA-256 checksums.
@@ -293,5 +223,3 @@ If you use these feature caches or RAVEL, please cite the paper and the relevant
   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).