| --- |
| dataset_info: |
| - config_name: DINO |
| features: |
| - name: random_crop |
| dtype: bool |
| - name: epochs |
| dtype: int64 |
| - name: seed |
| dtype: int64 |
| - name: best_checkpoint_test_loss |
| dtype: float64 |
| - name: model_idx |
| dtype: int64 |
| - name: dataset_name |
| dtype: string |
| - name: best_checkpoint_test_accuracy |
| dtype: float64 |
| - name: weight_decay |
| dtype: float64 |
| - name: batch_size |
| dtype: int64 |
| - name: base_model |
| dtype: string |
| - name: best_checkpoint_val_loss |
| dtype: float64 |
| - name: dataset_chosen_targets |
| dtype: string |
| - name: best_checkpoint_train_accuracy |
| dtype: float64 |
| - name: best_checkpoint_train_loss |
| dtype: float64 |
| - name: max_train_steps |
| dtype: int64 |
| - name: best_checkpoint_val_accuracy |
| dtype: float64 |
| - name: lr_scheduler |
| dtype: string |
| - name: learning_rate |
| dtype: float64 |
| - name: random_flip |
| dtype: bool |
| - name: split |
| dtype: string |
| - name: subset |
| dtype: string |
| - name: hf_model_id |
| dtype: string |
| - name: hf_model_url |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 556679 |
| num_examples: 701 |
| - name: val |
| num_bytes: 78880 |
| num_examples: 100 |
| - name: test |
| num_bytes: 159344 |
| num_examples: 201 |
| download_size: 253029 |
| dataset_size: 794903 |
| - config_name: MAE |
| features: |
| - name: random_crop |
| dtype: bool |
| - name: epochs |
| dtype: int64 |
| - name: seed |
| dtype: int64 |
| - name: best_checkpoint_test_loss |
| dtype: float64 |
| - name: model_idx |
| dtype: int64 |
| - name: dataset_name |
| dtype: string |
| - name: best_checkpoint_test_accuracy |
| dtype: float64 |
| - name: weight_decay |
| dtype: float64 |
| - name: batch_size |
| dtype: int64 |
| - name: base_model |
| dtype: string |
| - name: best_checkpoint_val_loss |
| dtype: float64 |
| - name: dataset_chosen_targets |
| dtype: string |
| - name: best_checkpoint_train_accuracy |
| dtype: float64 |
| - name: best_checkpoint_train_loss |
| dtype: float64 |
| - name: max_train_steps |
| dtype: int64 |
| - name: best_checkpoint_val_accuracy |
| dtype: float64 |
| - name: lr_scheduler |
| dtype: string |
| - name: learning_rate |
| dtype: float64 |
| - name: random_flip |
| dtype: bool |
| - name: split |
| dtype: string |
| - name: subset |
| dtype: string |
| - name: hf_model_id |
| dtype: string |
| - name: hf_model_url |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 553950 |
| num_examples: 701 |
| - name: val |
| num_bytes: 79028 |
| num_examples: 100 |
| - name: test |
| num_bytes: 158815 |
| num_examples: 201 |
| download_size: 253409 |
| dataset_size: 791793 |
| - config_name: ResNet |
| features: |
| - name: random_crop |
| dtype: bool |
| - name: epochs |
| dtype: int64 |
| - name: seed |
| dtype: int64 |
| - name: best_checkpoint_test_loss |
| dtype: float64 |
| - name: model_idx |
| dtype: int64 |
| - name: dataset_name |
| dtype: string |
| - name: best_checkpoint_test_accuracy |
| dtype: float64 |
| - name: weight_decay |
| dtype: float64 |
| - name: batch_size |
| dtype: int64 |
| - name: base_model |
| dtype: string |
| - name: best_checkpoint_val_loss |
| dtype: float64 |
| - name: dataset_chosen_targets |
| dtype: string |
| - name: best_checkpoint_train_accuracy |
| dtype: float64 |
| - name: best_checkpoint_train_loss |
| dtype: float64 |
| - name: max_train_steps |
| dtype: int64 |
| - name: best_checkpoint_val_accuracy |
| dtype: float64 |
| - name: lr_scheduler |
| dtype: string |
| - name: learning_rate |
| dtype: float64 |
| - name: random_flip |
| dtype: bool |
| - name: split |
| dtype: string |
| - name: subset |
| dtype: string |
| - name: hf_model_id |
| dtype: string |
| - name: hf_model_url |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 559861 |
| num_examples: 701 |
| - name: val |
| num_bytes: 79621 |
| num_examples: 100 |
| - name: test |
| num_bytes: 160334 |
| num_examples: 201 |
| download_size: 254554 |
| dataset_size: 799816 |
| - config_name: SD_1k |
| features: |
| - name: model_idx |
| dtype: int64 |
| - name: imagenet_class_id |
| dtype: string |
| - name: imagenet_class_name |
| dtype: string |
| - name: split |
| dtype: string |
| - name: subset |
| dtype: string |
| - name: seed |
| dtype: int64 |
| - name: learning_rate |
| dtype: float64 |
| - name: max_train_steps |
| dtype: int64 |
| - name: rank |
| dtype: int64 |
| - name: pretrained_model_name_or_path |
| dtype: string |
| - name: n_training_samples |
| dtype: int64 |
| - name: hf_model_id |
| dtype: string |
| - name: hf_model_url |
| dtype: string |
| - name: hf_model_path |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 906691 |
| num_examples: 3500 |
| - name: val |
| num_bytes: 64114 |
| num_examples: 251 |
| - name: test |
| num_bytes: 128377 |
| num_examples: 499 |
| - name: val_holdout |
| num_bytes: 67364 |
| num_examples: 249 |
| - name: test_holdout |
| num_bytes: 137229 |
| num_examples: 501 |
| download_size: 198659 |
| dataset_size: 1303775 |
| - config_name: SD_200 |
| features: |
| - name: model_idx |
| dtype: int64 |
| - name: imagenet_class_id |
| dtype: string |
| - name: imagenet_class_name |
| dtype: string |
| - name: split |
| dtype: string |
| - name: subset |
| dtype: string |
| - name: seed |
| dtype: int64 |
| - name: learning_rate |
| dtype: float64 |
| - name: max_train_steps |
| dtype: int64 |
| - name: rank |
| dtype: int64 |
| - name: pretrained_model_name_or_path |
| dtype: string |
| - name: n_training_samples |
| dtype: int64 |
| - name: hf_model_id |
| dtype: string |
| - name: hf_model_url |
| dtype: string |
| - name: hf_model_path |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 924063 |
| num_examples: 3500 |
| - name: val |
| num_bytes: 65187 |
| num_examples: 251 |
| - name: test |
| num_bytes: 130863 |
| num_examples: 499 |
| - name: val_holdout |
| num_bytes: 68302 |
| num_examples: 249 |
| - name: test_holdout |
| num_bytes: 138450 |
| num_examples: 501 |
| download_size: 158079 |
| dataset_size: 1326865 |
| - config_name: SupViT |
| features: |
| - name: random_crop |
| dtype: bool |
| - name: epochs |
| dtype: int64 |
| - name: seed |
| dtype: int64 |
| - name: best_checkpoint_test_loss |
| dtype: float64 |
| - name: model_idx |
| dtype: int64 |
| - name: dataset_name |
| dtype: string |
| - name: best_checkpoint_test_accuracy |
| dtype: float64 |
| - name: weight_decay |
| dtype: float64 |
| - name: batch_size |
| dtype: int64 |
| - name: base_model |
| dtype: string |
| - name: best_checkpoint_val_loss |
| dtype: float64 |
| - name: dataset_chosen_targets |
| dtype: string |
| - name: best_checkpoint_train_accuracy |
| dtype: float64 |
| - name: best_checkpoint_train_loss |
| dtype: float64 |
| - name: max_train_steps |
| dtype: int64 |
| - name: best_checkpoint_val_accuracy |
| dtype: float64 |
| - name: lr_scheduler |
| dtype: string |
| - name: learning_rate |
| dtype: float64 |
| - name: random_flip |
| dtype: bool |
| - name: split |
| dtype: string |
| - name: subset |
| dtype: string |
| - name: hf_model_id |
| dtype: string |
| - name: hf_model_url |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 562795 |
| num_examples: 698 |
| - name: val |
| num_bytes: 79433 |
| num_examples: 99 |
| - name: test |
| num_bytes: 161793 |
| num_examples: 201 |
| download_size: 248900 |
| dataset_size: 804021 |
| 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-* |
| - split: val_holdout |
| 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-* |
| - split: val_holdout |
| path: SD_200/val_holdout-* |
| - 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} |
| } |
| ``` |
| |
