README.md

---
base_model: google/vit-base-patch16-224
library_name: transformers
pipeline_tag: image-classification
tags:
  - probex
  - model-j
  - weight-space-learning
---

# Model-J: SupViT Model (model_idx_0728)

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** | SupViT |
| **Split** | train |
| **Base Model** | `google/vit-base-patch16-224` |
| **Dataset** | CIFAR100 (50 classes) |

## Training Hyperparameters

| Parameter | Value |
|---|---|
| Learning Rate | 7e-05 |
| LR Scheduler | cosine_with_restarts |
| Epochs | 4 |
| Max Train Steps | 1332 |
| Batch Size | 64 |
| Weight Decay | 0.005 |
| Seed | 728 |
| Random Crop | True |
| Random Flip | False |

## Performance

| Metric | Value |
|---|---|
| Train Accuracy | 0.9980 |
| Val Accuracy | 0.9629 |
| Test Accuracy | 0.9566 |

## Training Categories

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

`lizard`, `possum`, `squirrel`, `snail`, `boy`, `cattle`, `pear`, `lion`, `palm_tree`, `road`, `pickup_truck`, `sea`, `beetle`, `can`, `willow_tree`, `woman`, `oak_tree`, `apple`, `camel`, `wardrobe`, `telephone`, `lawn_mower`, `orchid`, `train`, `whale`, `tulip`, `clock`, `kangaroo`, `beaver`, `chimpanzee`, `shark`, `snake`, `dinosaur`, `fox`, `raccoon`, `spider`, `rabbit`, `sweet_pepper`, `skyscraper`, `aquarium_fish`, `lobster`, `table`, `bear`, `rose`, `cloud`, `ray`, `bottle`, `hamster`, `tank`, `crocodile`