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README.md

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----
-license: mit
----
+---
+license: cc-by-sa-4.0
+task_categories:
+  - image-classification
+  - zero-shot-classification
+language:
+  - en
+tags:
+  - vision-language
+  - CLIP
+  - out-of-pre-training
+  - OOP
+  - benchmark
+  - multimodal
+  - few-shot
+  - zero-shot
+pretty_name: LAION-Beyond
+size_categories:
+  - 100K<n<1M
+---
+
+# LAION-Beyond
+
+**LAION-Beyond: Reproducible Vision-Language Models Meet Concepts Out of Pre-Training**
+
+📄 [Paper (CVPR 2025)](https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Reproducible_Vision-Language_Models_Meet_Concepts_Out_of_Pre-Training_CVPR_2025_paper.pdf) | 💻 [Code](https://github.com/M-HuangX/LAION-Beyond) | 🌐 [Project Page](https://m-huangx.github.io/laion_beyond/)
+
+---
+
+## Dataset Summary
+
+LAION-Beyond is the **first multi-domain benchmark** specifically designed to evaluate the Out-of-Pre-training (OOP) generalization of vision-language models (e.g., CLIP, OpenCLIP, EVA-CLIP).
+
+We distinguish two types of visual concepts:
+- **IP (In-Pre-training)**: concepts that appear in the pre-training data (e.g., LAION-400M / 2B / 5B)
+- **OOP (Out-of-Pre-training)**: concepts entirely absent from the pre-training data
+
+The key finding of our paper is that despite OpenCLIP's image encoder forming well-separated clusters for OOP concepts, **zero-shot transfer fails significantly** due to poor image-text alignment — the token embeddings for OOP class names were never aligned with visual features during pre-training.
+
+---
+
+## Dataset Statistics
+
+| Split | Images | Concepts |
+|-------|--------|----------|
+| OOP   | 106,052 | 674      |
+| IP    | 51,330  | 324      |
+| **Total** | **157,382** | **998** |
+
+The dataset spans **9 diverse domains**:
+- 🐾 Animals
+- 🏛️ Architecture
+- 👘 Attire
+- 🎨 FolkArt
+- 🍜 Food
+- 🦋 Insects & Spiders
+- 🗺️ Landmark
+- 🌿 Plants & Fungi
+- 🎮 Pokemon
+
+Each domain contains an IP subset and an OOP subset, covering LAION-400M, LAION-2B, and LAION-5B scales to support neural scaling law research.
+
+---
+
+## Dataset Structure
+
+Each domain folder is named `{Domain}{NumClasses}_{IP/OOP}`, e.g., `Animals42_IP`, `Animals92_OOP`.
+
+```
+LAION_Beyond/
+├── Animals42_IP/
+│   ├── images/                        # jpg images organized by class
+│   ├── label2name.json                # label index → class name
+│   ├── name2label.json                # class name → label index
+│   ├── merged_mapping.json            # merged label mapping
+│   └── split_Xin_Animals42_IP.json    # train/val/test split info
+├── Animals92_OOP/
+│   └── ...
+├── Architecture23_IP/
+├── Architecture50_OOP/
+├── Attire28_IP/
+├── Attire54_OOP/
+├── FolkArt27_IP/
+├── FolkArt59_OOP/
+├── Food27_IP/
+├── Food53_OOP/
+├── Insects_Spiders52_IP/
+├── Insects_Spiders106_OOP/
+├── Landmark30_IP/
+├── Landmark59_OOP/
+├── Plants_Fugi56_IP/
+├── Plants_Fugi113_OOP/
+├── Pokemon39_IP/
+└── Pokemon89_OOP/
+```
+
+### File Descriptions
+
+| File | Description |
+|------|-------------|
+| `images/` | Raw image files (JPG), organized by class subfolder |
+| `label2name.json` | Mapping from integer label to class name string |
+| `name2label.json` | Mapping from class name string to integer label |
+| `merged_mapping.json` | Combined label mapping across splits |
+| `split_Xin_*.json` | Train / val / test split assignments per image |
+
+---
+
+## Loading the Dataset
+
+### Option 1: Direct file access (recommended)
+
+```python
+import json
+import os
+from PIL import Image
+
+domain = "Animals42_IP"
+root = f"path/to/LAION_Beyond/{domain}"
+
+# Load label mapping
+with open(os.path.join(root, "label2name.json")) as f:
+    label2name = json.load(f)
+
+# Load split info
+with open(os.path.join(root, f"split_Xin_{domain}.json")) as f:
+    split_info = json.load(f)
+
+# Load an image
+img = Image.open(os.path.join(root, "images", "some_class", "image.jpg"))
+```
+
+### Option 2: Using HuggingFace `datasets`
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("MHuangX/LAION-Beyond")
+```
+
+---
+
+## Key Findings
+
+1. **Strong image features for OOP concepts**: OpenCLIP's image encoder forms well-separated clusters for OOP concepts (clustering accuracy gap < 3% on most domains vs. IP concepts).
+
+2. **Image-text alignment failure**: Zero-shot accuracy on OOP concepts is significantly lower than IP concepts, persisting even as pre-training data scales from 400M to 5B.
+
+3. **Name-tuning is the key**: Our proposed FSNL and ZSNL algorithms, which fine-tune only the name (token) embeddings of OOP concepts, efficiently restore OOP generalization without degrading IP performance.
+
+---
+
+## Algorithms
+
+### FSNL — Few-Shot Name Learning
+Optimizes only OOP concept name embeddings using a few image-text pairs, with context augmentation via similar concept shuffling. Achieves state-of-the-art on 8/9 domains.
+
+### ZSNL — Zero-Shot Name Learning
+Requires no image-text pairs. Uses Novel Class Discovery (NCD) and image-text bipartite graph matching to optimize OOP name embeddings from unlabeled images only.
+
+---
+
+## Benchmark Results (400M split)
+
+### OOP Few-Shot Learning (4-shot, H-mean of OOP & IP accuracy)
+
+| Method | Animals | Architecture | Attire | FolkArt | Food | Insects | Landmark | Plants | Pokemon | Avg |
+|--------|---------|--------------|--------|---------|------|---------|---------|--------|---------|-----|
+| OpenCLIP | 26.75 | 30.75 | 25.88 | 35.04 | 15.36 | 22.38 | 40.25 | 21.43 | 24.48 | 26.92 |
+| CoOp | 31.37 | 57.8 | 50.39 | 52.06 | 42.55 | 25.73 | 85.89 | 24.78 | 35.52 | 45.12 |
+| CLIP-Adapter | 38.98 | 59.27 | 64.56 | 56.32 | 64.32 | 32.51 | 90.82 | 31.97 | 54.99 | 54.86 |
+| **FSNL (ours)** | **46.17** | **62.63** | **71.65** | **63.03** | **70.0** | **44.03** | **94.48** | **44.12** | **68.87** | **62.55** |
+
+---
+
+## Citation
+
+If you use LAION-Beyond in your research, please cite:
+
+```bibtex
+@inproceedings{chen2025laionbeyond,
+    title={Reproducible Vision-Language Models Meet Concepts Out of Pre-Training},
+    author={Chen, Ziliang and Huang, Xin and Fan, Xiaoxuan and Wang, Keze and Zhou, Yuyu and Guan, Quanlong and Lin, Liang},
+    booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+    year={2025}
+}
+```
+
+---
+
+## License
+
+This dataset is released under the [Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0)](http://creativecommons.org/licenses/by-sa/4.0/).
+
+---
+
+## Authors
+
+[Xin Huang](https://www.linkedin.com/in/mhuangx/)†, [Ziliang Chen](https://scholar.google.com/citations?user=RC-LN4QAAAAJ&hl=en)†, Xiaoxuan Fan, [Keze Wang](https://kezewang.com/), Yuyu Zhou, [Quanlong Guan](https://scholar.google.com/citations?user=v4JiSqsAAAAJ&hl=en), [Liang Lin](http://www.linliang.net/)*
+
+Affiliations: Peng Cheng Laboratory, Sun Yat-sen University, EPFL, Jinan University
+
+†Equal Contribution · *Corresponding Author