| base_model: facebook/vit-mae-base | |
| library_name: transformers | |
| pipeline_tag: image-classification | |
| tags: | |
| - probex | |
| - model-j | |
| - weight-space-learning | |
| # Model-J: MAE Model (model_idx_0957) | |
| This model is part of 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">Dataset</a> | |
| </p> | |
|  | |
| ## Model Details | |
| | Attribute | Value | | |
| |---|---| | |
| | **Subset** | MAE | | |
| | **Split** | train | | |
| | **Base Model** | `facebook/vit-mae-base` | | |
| | **Dataset** | CIFAR100 (50 classes) | | |
| ## Training Hyperparameters | |
| | Parameter | Value | | |
| |---|---| | |
| | Learning Rate | 7e-05 | | |
| | LR Scheduler | constant_with_warmup | | |
| | Epochs | 5 | | |
| | Max Train Steps | 1665 | | |
| | Batch Size | 64 | | |
| | Weight Decay | 0.007 | | |
| | Seed | 957 | | |
| | Random Crop | True | | |
| | Random Flip | True | | |
| ## Performance | |
| | Metric | Value | | |
| |---|---| | |
| | Train Accuracy | 0.9637 | | |
| | Val Accuracy | 0.8563 | | |
| | Test Accuracy | 0.8620 | | |
| ## Training Categories | |
| The model was fine-tuned on the following 50 CIFAR100 classes: | |
| `otter`, `porcupine`, `cloud`, `whale`, `tank`, `seal`, `butterfly`, `beetle`, `snake`, `chimpanzee`, `bed`, `bear`, `crocodile`, `lizard`, `hamster`, `train`, `pear`, `dolphin`, `rose`, `lobster`, `keyboard`, `rocket`, `plain`, `maple_tree`, `television`, `shark`, `girl`, `camel`, `bus`, `flatfish`, `can`, `orange`, `worm`, `streetcar`, `pickup_truck`, `bowl`, `palm_tree`, `tulip`, `wardrobe`, `chair`, `sunflower`, `raccoon`, `clock`, `baby`, `mountain`, `road`, `squirrel`, `shrew`, `leopard`, `motorcycle` | |