---
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_0930)

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 | 3e-05 |
| LR Scheduler | linear |
| Epochs | 3 |
| Max Train Steps | 999 |
| Batch Size | 64 |
| Weight Decay | 0.009 |
| Seed | 930 |
| Random Crop | True |
| Random Flip | True |

## Performance

| Metric | Value |
|---|---|
| Train Accuracy | 0.9968 |
| Val Accuracy | 0.9355 |
| Test Accuracy | 0.9418 |

## Training Categories

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

`trout`, `bear`, `castle`, `road`, `lobster`, `chimpanzee`, `fox`, `ray`, `whale`, `squirrel`, `crocodile`, `bus`, `sea`, `bridge`, `streetcar`, `dinosaur`, `turtle`, `lawn_mower`, `orange`, `pickup_truck`, `mouse`, `pine_tree`, `butterfly`, `rabbit`, `motorcycle`, `sweet_pepper`, `aquarium_fish`, `worm`, `beaver`, `spider`, `telephone`, `palm_tree`, `porcupine`, `pear`, `caterpillar`, `sunflower`, `leopard`, `oak_tree`, `raccoon`, `skyscraper`, `television`, `crab`, `keyboard`, `baby`, `dolphin`, `tractor`, `skunk`, `wardrobe`, `cup`, `clock`