Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+---
+
+# HC-Bench
+
+**HC-Bench** is a compact multi-part image benchmark for evaluating recognition and prompting robustness, especially in **hidden-content** scenes. It contains:
+
+- **object/** — 56 base images and 56 *hidden* variants of the same lemmas, plus prompts and metadata.
+- **text/** — 56 Latin/English and 56 Chinese lemma–description pairs with matching PNGs.
+- **wild/** — 53 in-the-wild images for additional generalization checks.
+
+---
+
+## Repository structure
+
+```
+
+HC-Bench/
+├─ object/
+│  ├─ base/                     # 56 base images (7 types × 8 lemmas)
+│  ├─ hidden/                   # 56 hidden-content variants (same lemmas)
+│  ├─ image\_base.txt            # 7 types and their 8 lemmas each
+│  ├─ image\_generate\_prompts.txt# per-lemma scene prompts used for generation
+│  └─ lemmas\_descriptions.json  # \[{Type, Lemma, Description}] × 56
+├─ text/
+│  ├─ Latin/                    # 28 English PNGs
+│  ├─ Chinese/                  # 28 Chinese PNGs
+│  ├─ English\_text.json         # 56 entries (Type, Length, Rarity, Lemma, Description)
+│  └─ Chinese\_text.json         # 56 entries (Type, Length, Rarity, Lemma, Description)
+└─ wild/                        # 53 PNGs
+
+````
+
+---
+
+## Contents
+
+### `object/`
+- **`base/`**: Canonical image per lemma (e.g., `Apple.jpg`, `Einstein.png`).
+- **`hidden/`**: Composite/camouflaged image for the *same* lemma set (e.g., `apple.png`, `einstein.png`).
+- **`image_base.txt`**: The 7 high-level types and their 8 lemmas each (Humans, Species, Buildings, Cartoon, Furniture, Transports, Food).
+- **`image_generate_prompts.txt`**: Per-lemma prompts used to compose/generate scenes (e.g., *“A monorail cutting through a futuristic city with elevated walkways”* for `notredame`).
+- **`lemmas_descriptions.json`**: Minimal metadata with `{Type, Lemma, Description}` aligned 1:1 with the 56 lemmas.
+
+### `text/`
+- **`Latin/`** & **`Chinese/`**: 28 images each (total 56).
+- **`English_text.json`** & **`Chinese_text.json`**: 56-entry lists pairing lemmas to descriptions in two languages.  
+  (Note: The `English_text.json`/`Chinese_text.json` files include extra fields `Length` and `Rarity` for flexibility.)
+
+### `wild/`
+- 53 natural/urban scenes for robustness and transfer evaluation.
+
+---
+
+## Quick start (🤗 Datasets)
+
+> HC-Bench uses the **ImageFolder**/“imagefolder” style. Class labels are inferred from directory names when present (e.g., `base`, `hidden`). If you prefer raw images without labels, pass `drop_labels=True`.
+
+### Load **object/base** and **object/hidden**
+```python
+from datasets import load_dataset
+
+base = load_dataset(
+    "imagefolder",
+    data_files="https://huggingface.co/datasets/JohnnyZeppelin/HC-Bench/resolve/main/object/base/*",
+    split="train",
+    drop_labels=True,  # drop automatic label inference
+)
+
+hidden = load_dataset(
+    "imagefolder",
+    data_files="https://huggingface.co/datasets/JohnnyZeppelin/HC-Bench/resolve/main/object/hidden/*",
+    split="train",
+    drop_labels=True,
+)
+````
+
+### Load **wild/**
+
+```python
+wild = load_dataset(
+    "imagefolder",
+    data_files="https://huggingface.co/datasets/JohnnyZeppelin/HC-Bench/resolve/main/wild/*",
+    split="train",
+    drop_labels=True,
+)
+```
+
+### Load the **JSON** metadata (English/Chinese)
+
+```python
+from datasets import load_dataset
+
+en = load_dataset(
+    "json",
+    data_files="https://huggingface.co/datasets/JohnnyZeppelin/HC-Bench/resolve/main/text/English_text.json",
+    split="train",
+)
+zh = load_dataset(
+    "json",
+    data_files="https://huggingface.co/datasets/JohnnyZeppelin/HC-Bench/resolve/main/text/Chinese_text.json",
+    split="train",
+)
+```
+
+> Docs reference: `load_dataset` for JSON & files, and ImageFolder for image datasets.
+
+---
+
+## Pairing base/hidden with metadata
+
+Filenames differ in casing/spaces between `base/` (`Apple.jpg`) and `hidden/` (`apple.png`). Use `object/lemmas_descriptions.json` as the canonical list of 56 lemmas and join by `Lemma`:
+
+```python
+import pandas as pd
+from datasets import load_dataset
+
+# 1) Canonical lemma list
+lemmas = load_dataset(
+    "json",
+    data_files="https://huggingface.co/datasets/JohnnyZeppelin/HC-Bench/resolve/main/object/lemmas_descriptions.json",
+    split="train",
+).to_pandas()
+
+# 2) Build (lemma -> file) maps
+def to_lemma(name):  # normalize filenames to lemma
+    import re, os
+    stem = os.path.splitext(os.path.basename(name))[0]
+    return re.sub(r"\s+", "", stem).lower()
+
+base_ds = load_dataset(
+    "imagefolder",
+    data_files="https://huggingface.co/datasets/JohnnyZeppelin/HC-Bench/resolve/main/object/base/*",
+    split="train",
+    drop_labels=True,
+)
+hidden_ds = load_dataset(
+    "imagefolder",
+    data_files="https://huggingface.co/datasets/JohnnyZeppelin/HC-Bench/resolve/main/object/hidden/*",
+    split="train",
+    drop_labels=True,
+)
+
+import os
+base_map  = {to_lemma(x["image"].filename): x["image"] for x in base_ds}
+hidden_map= {to_lemma(x["image"].filename): x["image"] for x in hidden_ds}
+
+# 3) Join
+lemmas["base_image"]   = lemmas["Lemma"].apply(lambda L: base_map.get(L.lower()))
+lemmas["hidden_image"] = lemmas["Lemma"].apply(lambda L: hidden_map.get(L.lower()))
+```
+
+---
+
+
+
+---
+
+## Statistics
+
+* `object/base`: 56 images
+* `object/hidden`: 56 images
+* `text/Latin`: 28 images
+* `text/Chinese`: 28 images
+* `wild`: 53 images
+
+---
+
+## Citation
+
+If you use **HC-Bench**, please cite:
+
+```bibtex
+@misc{li2025semvinkadvancingvlmssemantic,
+      title={SemVink: Advancing VLMs' Semantic Understanding of Optical Illusions via Visual Global Thinking}, 
+      author={Sifan Li and Yujun Cai and Yiwei Wang},
+      year={2025},
+      eprint={2506.02803},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2506.02803}, 
+}
+```
+
+---
+
+
+
+