---
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_0037)

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>

![ProbeX](https://raw.githubusercontent.com/eliahuhorwitz/ProbeX/main/imgs/poster.png)

## Model Details

| Attribute | Value |
|---|---|
| **Subset** | MAE |
| **Split** | train |
| **Base Model** | `facebook/vit-mae-base` |
| **Dataset** | CIFAR100 (50 classes) |

## Training Hyperparameters

| Parameter | Value |
|---|---|
| Learning Rate | 3e-05 |
| LR Scheduler | constant |
| Epochs | 5 |
| Max Train Steps | 1665 |
| Batch Size | 64 |
| Weight Decay | 0.005 |
| Seed | 37 |
| Random Crop | False |
| Random Flip | True |

## Performance

| Metric | Value |
|---|---|
| Train Accuracy | 0.9819 |
| Val Accuracy | 0.8853 |
| Test Accuracy | 0.8822 |

## Training Categories

The model was fine-tuned on the following 50 CIFAR100 classes:

`trout`, `telephone`, `crocodile`, `bridge`, `lamp`, `bear`, `cup`, `cloud`, `raccoon`, `cattle`, `couch`, `lion`, `porcupine`, `fox`, `wardrobe`, `aquarium_fish`, `tulip`, `worm`, `beetle`, `bed`, `maple_tree`, `skunk`, `spider`, `rocket`, `shark`, `bottle`, `squirrel`, `snake`, `lobster`, `skyscraper`, `tractor`, `dinosaur`, `tiger`, `plate`, `crab`, `apple`, `pear`, `bee`, `bowl`, `kangaroo`, `hamster`, `shrew`, `caterpillar`, `turtle`, `sunflower`, `road`, `streetcar`, `baby`, `palm_tree`, `seal`