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_0543)

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 | 0.0003 |
| LR Scheduler | constant_with_warmup |
| Epochs | 3 |
| Max Train Steps | 999 |
| Batch Size | 64 |
| Weight Decay | 0.007 |
| Seed | 543 |
| Random Crop | True |
| Random Flip | False |

## Performance

| Metric | Value |
|---|---|
| Train Accuracy | 0.8978 |
| Val Accuracy | 0.7755 |
| Test Accuracy | 0.7768 |

## Training Categories

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

`sea`, `bed`, `porcupine`, `cloud`, `dinosaur`, `clock`, `rocket`, `ray`, `lion`, `cockroach`, `snake`, `possum`, `girl`, `skyscraper`, `willow_tree`, `chair`, `maple_tree`, `mouse`, `bowl`, `shark`, `bridge`, `fox`, `squirrel`, `man`, `tractor`, `butterfly`, `palm_tree`, `telephone`, `rose`, `can`, `mountain`, `beetle`, `orchid`, `boy`, `tulip`, `spider`, `sweet_pepper`, `orange`, `wardrobe`, `caterpillar`, `skunk`, `snail`, `plate`, `castle`, `woman`, `table`, `couch`, `cup`, `lawn_mower`, `aquarium_fish`