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_0495)

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 | 0.0005 |
| LR Scheduler | cosine_with_restarts |
| Epochs | 6 |
| Max Train Steps | 1998 |
| Batch Size | 64 |
| Weight Decay | 0.01 |
| Seed | 495 |
| Random Crop | False |
| Random Flip | True |

## Performance

| Metric | Value |
|---|---|
| Train Accuracy | 0.9990 |
| Val Accuracy | 0.9192 |
| Test Accuracy | 0.9122 |

## Training Categories

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

`train`, `mouse`, `house`, `bed`, `bear`, `whale`, `crocodile`, `maple_tree`, `rocket`, `bicycle`, `snake`, `snail`, `porcupine`, `poppy`, `couch`, `motorcycle`, `road`, `telephone`, `girl`, `spider`, `lamp`, `clock`, `can`, `lawn_mower`, `pear`, `television`, `shrew`, `shark`, `palm_tree`, `orange`, `mountain`, `sea`, `tulip`, `leopard`, `cloud`, `crab`, `orchid`, `seal`, `oak_tree`, `tractor`, `forest`, `plate`, `camel`, `castle`, `cattle`, `cup`, `wolf`, `pickup_truck`, `tiger`, `otter`