README.md

---
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_0326)

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 | 0.0001 |
| LR Scheduler | cosine |
| Epochs | 9 |
| Max Train Steps | 2997 |
| Batch Size | 64 |
| Weight Decay | 0.009 |
| Seed | 326 |
| Random Crop | False |
| Random Flip | False |

## Performance

| Metric | Value |
|---|---|
| Train Accuracy | 0.9999 |
| Val Accuracy | 0.8875 |
| Test Accuracy | 0.8866 |

## Training Categories

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

`maple_tree`, `ray`, `caterpillar`, `telephone`, `lamp`, `worm`, `fox`, `lawn_mower`, `orange`, `whale`, `trout`, `porcupine`, `motorcycle`, `lion`, `lobster`, `chimpanzee`, `train`, `seal`, `cup`, `bus`, `pine_tree`, `otter`, `poppy`, `sea`, `mountain`, `pear`, `tractor`, `orchid`, `bear`, `woman`, `television`, `cattle`, `road`, `bed`, `oak_tree`, `house`, `baby`, `wardrobe`, `raccoon`, `shark`, `turtle`, `bridge`, `kangaroo`, `spider`, `butterfly`, `mouse`, `tulip`, `girl`, `can`, `castle`