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_0016)

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 | 0.0005 |
| LR Scheduler | cosine_with_restarts |
| Epochs | 9 |
| Max Train Steps | 2997 |
| Batch Size | 64 |
| Weight Decay | 0.03 |
| Seed | 16 |
| Random Crop | False |
| Random Flip | False |

## Performance

| Metric | Value |
|---|---|
| Train Accuracy | 0.9606 |
| Val Accuracy | 0.5357 |
| Test Accuracy | 0.5438 |

## Training Categories

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

`pickup_truck`, `camel`, `chimpanzee`, `forest`, `lion`, `wardrobe`, `bear`, `raccoon`, `lobster`, `cloud`, `kangaroo`, `train`, `castle`, `streetcar`, `tank`, `lamp`, `spider`, `sunflower`, `lawn_mower`, `telephone`, `can`, `chair`, `caterpillar`, `pine_tree`, `bee`, `oak_tree`, `cattle`, `couch`, `road`, `dolphin`, `rabbit`, `television`, `ray`, `rocket`, `plain`, `cockroach`, `man`, `tiger`, `keyboard`, `beetle`, `fox`, `cup`, `dinosaur`, `squirrel`, `sea`, `bowl`, `apple`, `baby`, `trout`, `hamster`