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---
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_0196)
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> | πŸ“ƒ <a href="https://arxiv.org/abs/2410.13569" target="_blank">Paper</a> | πŸ’» <a href="https://github.com/eliahuhorwitz/ProbeX" target="_blank">GitHub</a> | πŸ€— <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** | test |
| **Base Model** | `facebook/vit-mae-base` |
| **Dataset** | CIFAR100 (50 classes) |
## Training Hyperparameters
| Parameter | Value |
|---|---|
| Learning Rate | 0.0003 |
| LR Scheduler | constant_with_warmup |
| Epochs | 5 |
| Max Train Steps | 1665 |
| Batch Size | 64 |
| Weight Decay | 0.007 |
| Seed | 196 |
| Random Crop | True |
| Random Flip | True |
## Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9146 |
| Val Accuracy | 0.7781 |
| Test Accuracy | 0.7780 |
## Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
`bridge`, `beetle`, `seal`, `bee`, `snail`, `aquarium_fish`, `worm`, `raccoon`, `lobster`, `clock`, `flatfish`, `crab`, `possum`, `table`, `hamster`, `caterpillar`, `kangaroo`, `bowl`, `otter`, `pine_tree`, `butterfly`, `ray`, `maple_tree`, `can`, `orchid`, `baby`, `snake`, `plain`, `shark`, `pickup_truck`, `cup`, `cattle`, `sea`, `wardrobe`, `cloud`, `sunflower`, `trout`, `cockroach`, `keyboard`, `elephant`, `palm_tree`, `poppy`, `woman`, `rose`, `mouse`, `lawn_mower`, `lamp`, `sweet_pepper`, `fox`, `rocket`