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Datasets:
MHuangX
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LAION-Beyond

Tasks:
Image Classification
Zero-Shot Classification
Modalities:
Image
Languages:
English
Size:
100K<n<1M
Tags:
vision-language
CLIP
out-of-pre-training
OOP
benchmark
multimodal
License:
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---
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: Reproducible Vision-Language Models Meet Concepts Out of Pre-Training
<p align="center">
 📄 <a href="https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Reproducible_Vision-Language_Models_Meet_Concepts_Out_of_Pre-Training_CVPR_2025_paper.pdf">Paper (CVPR 2025)</a> |
💻 <a href="https://github.com/M-HuangX/LAION-Beyond">Code</a> |
🌐 <a href="https://github.com/M-HuangX/laion_beyond">Project Page</a>
</p>
## 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
<p align="center">
 <img src="https://raw.githubusercontent.com/M-HuangX/laion_beyond/master/static/images/Figure1_OOP_IP_difference.jpg" alt="IP vs OOP Difference" width="80%">
 <br>
 <em>Figure 1: Comparison between IP and OOP generalization. The former evaluates generalization within seen visual concepts, while the latter tests concepts absent during pre-training.</em>
</p>
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** |
<p align="center">
 <img src="https://raw.githubusercontent.com/M-HuangX/laion_beyond/master/static/images/Figure2a_LAION_Beyond_Distribution.png" width="48%">
 <img src="https://raw.githubusercontent.com/M-HuangX/laion_beyond/master/static/images/Figure2b_Image_Counts_per_category.png" width="48%">
 <br>
 <em>Figure 2: (Left) Statistics of OOP/IP concepts across different LAION scales; (Right) Detailed train/val/test split in LAION-Beyond (400M).</em>
</p>
### Domains Covered:
- 🐾 **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: Download full dataset (recommended)
```python
from huggingface_hub import snapshot_download
local_dir = snapshot_download(
 repo_id="MHuangX/LAION-Beyond",
 repo_type="dataset",
 local_dir="./LAION_Beyond"
)
```
### Option 2: Download a single domain only
```python
from huggingface_hub import snapshot_download
local_dir = snapshot_download(
 repo_id="MHuangX/LAION-Beyond",
 repo_type="dataset",
 local_dir="./LAION_Beyond",
 allow_patterns="Animals42_IP/**"
)
```
---
## 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{chen2025reproducible,
 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 Computer Vision and Pattern Recognition Conference},
 pages={14701--14711},
 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