| 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_0283) | |
| 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 | 5e-05 | | |
| | LR Scheduler | constant_with_warmup | | |
| | Epochs | 6 | | |
| | Max Train Steps | 1998 | | |
| | Batch Size | 64 | | |
| | Weight Decay | 0.05 | | |
| | Seed | 283 | | |
| | Random Crop | True | | |
| | Random Flip | False | | |
| ## Performance | |
| | Metric | Value | | |
| |---|---| | |
| | Train Accuracy | 0.9538 | | |
| | Val Accuracy | 0.8419 | | |
| | Test Accuracy | 0.8508 | | |
| ## Training Categories | |
| The model was fine-tuned on the following 50 CIFAR100 classes: | |
| `bicycle`, `house`, `castle`, `aquarium_fish`, `girl`, `streetcar`, `lawn_mower`, `worm`, `tulip`, `woman`, `table`, `caterpillar`, `telephone`, `kangaroo`, `butterfly`, `snake`, `dinosaur`, `mushroom`, `seal`, `raccoon`, `lizard`, `clock`, `mountain`, `porcupine`, `otter`, `trout`, `bed`, `fox`, `chimpanzee`, `plate`, `road`, `crocodile`, `motorcycle`, `elephant`, `snail`, `bridge`, `squirrel`, `man`, `train`, `possum`, `poppy`, `shark`, `tractor`, `bear`, `sea`, `rocket`, `cup`, `orange`, `shrew`, `lobster` | |