Datasets:
File size: 11,179 Bytes
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dataset_info:
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configs:
- config_name: DINO
data_files:
- split: train
path: DINO/train-*
- split: val
path: DINO/val-*
- split: test
path: DINO/test-*
- config_name: MAE
data_files:
- split: train
path: MAE/train-*
- split: val
path: MAE/val-*
- split: test
path: MAE/test-*
- config_name: ResNet
data_files:
- split: train
path: ResNet/train-*
- split: val
path: ResNet/val-*
- split: test
path: ResNet/test-*
- config_name: SD_1k
data_files:
- split: train
path: SD_1k/train-*
- split: val
path: SD_1k/val-*
- split: test
path: SD_1k/test-*
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path: SD_1k/val_holdout-*
- split: test_holdout
path: SD_1k/test_holdout-*
- config_name: SD_200
data_files:
- split: train
path: SD_200/train-*
- split: val
path: SD_200/val-*
- split: test
path: SD_200/test-*
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- split: test_holdout
path: SD_200/test_holdout-*
- config_name: SupViT
data_files:
- split: train
path: SupViT/train-*
- split: val
path: SupViT/val-*
- split: test
path: SupViT/test-*
tags:
- probex
- model-j
- weight-space-learning
- model-zoo
- hyperparameters
- stable-diffusion
- vit
- resnet
size_categories:
- 10K<n<100K
---
# Model-J Dataset
This dataset contains the hyperparameters, metadata, and Hugging Face links for all models in the **Model-J** dataset, introduced in:
**Learning on Model Weights using Tree Experts** (CVPR 2025) by Eliahu Horwitz*, Bar Cavia*, Jonathan Kahana*, Yedid Hoshen
<p align="center">
🌐 <a href="https://horwitz.ai/probex" target="_blank">Project</a> | 📃 <a href="https://arxiv.org/abs/2410.13569" target="_blank">Paper</a> | 💻 <a href="https://github.com/eliahuhorwitz/ProbeX" target="_blank">GitHub</a> | 🤗 <a href="https://huggingface.co/ProbeX" target="_blank">Models</a>
</p>
## Overview
Model-J is a large-scale dataset of trained neural networks designed for research on learning from model weights. It contains **14,004** models spanning 6 subsets, each with train/val/test splits. Every row in this dataset provides the full training hyperparameters, performance metrics, and a direct link to the corresponding model weights on Hugging Face.
## Subsets
### Discriminative (one model per HF repo)
| Subset | Base Model | Train | Val | Test | Total |
|---|---|---|---|---|---|
| **DINO** | `facebook/dino-vitb16` | 701 | 100 | 201 | 1,002 |
| **MAE** | `facebook/vit-mae-base` | 701 | 100 | 201 | 1,002 |
| **SupViT** | `google/vit-base-patch16-224` | 698 | 99 | 201 | 998 |
| **ResNet** | `microsoft/resnet-18` | 701 | 100 | 201 | 1,002 |
Each discriminative model is a full fine-tuned classifier hosted in its own Hugging Face repository. The `hf_model_id` and `hf_model_url` columns point directly to the model.
### Generative (bundled LoRA models in a single HF repo)
| Subset | Train | Val | Test | Val Holdout | Test Holdout | Total |
|---|---|-----|------|-------------|--------------|---|
| **SD_200** | 3,500 | 251 | 499 | 249 | 501 | 5,000 |
| **SD_1k** | 3,500 | 251 | 499 | 249 | 501 | 5,000 |
Each generative model is a LoRA adapter. All models within a subset are bundled into a single Hugging Face repository ([SD_1k](https://huggingface.co/ProbeX/Model-J__SD_1k), [SD_200](https://huggingface.co/ProbeX/Model-J__SD_200)). The `hf_model_path` column provides the path to each model's weights within the repo. Each model's directory also contains its training images.
## Citation
If you find this useful for your research, please use the following.
```
@InProceedings{Horwitz_2025_CVPR,
author = {Horwitz, Eliahu and Cavia, Bar and Kahana, Jonathan and Hoshen, Yedid},
title = {Learning on Model Weights using Tree Experts},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {20468-20478}
}
```
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