README.md

---
base_model: facebook/dino-vitb16
library_name: transformers
pipeline_tag: image-classification
tags:
  - probex
  - model-j
  - weight-space-learning
---

# Model-J: DINO Model (model_idx_0638)

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** | DINO |
| **Split** | train |
| **Base Model** | `facebook/dino-vitb16` |
| **Dataset** | CIFAR100 (50 classes) |

## Training Hyperparameters

| Parameter | Value |
|---|---|
| Learning Rate | 0.0005 |
| LR Scheduler | cosine_with_restarts |
| Epochs | 4 |
| Max Train Steps | 1332 |
| Batch Size | 64 |
| Weight Decay | 0.03 |
| Seed | 638 |
| Random Crop | False |
| Random Flip | False |

## Performance

| Metric | Value |
|---|---|
| Train Accuracy | 0.3286 |
| Val Accuracy | 0.2952 |
| Test Accuracy | 0.2986 |

## Training Categories

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

`bee`, `tank`, `possum`, `plate`, `skunk`, `lobster`, `snake`, `lamp`, `elephant`, `bridge`, `cup`, `butterfly`, `hamster`, `otter`, `keyboard`, `snail`, `trout`, `sweet_pepper`, `bear`, `train`, `castle`, `seal`, `road`, `flatfish`, `can`, `cattle`, `lawn_mower`, `table`, `woman`, `forest`, `man`, `bed`, `skyscraper`, `lizard`, `wardrobe`, `girl`, `streetcar`, `cockroach`, `crab`, `wolf`, `worm`, `willow_tree`, `rabbit`, `orchid`, `apple`, `telephone`, `squirrel`, `poppy`, `clock`, `bicycle`