---
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_0023)

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** | val |
| **Base Model** | `facebook/dino-vitb16` |
| **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 | 0.005 |
| Seed | 23 |
| Random Crop | False |
| Random Flip | False |

## Performance

| Metric | Value |
|---|---|
| Train Accuracy | 0.4817 |
| Val Accuracy | 0.3877 |
| Test Accuracy | 0.3860 |

## Training Categories

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

`plate`, `cattle`, `table`, `caterpillar`, `porcupine`, `sea`, `rabbit`, `tiger`, `seal`, `pine_tree`, `bicycle`, `camel`, `shark`, `cockroach`, `rocket`, `chimpanzee`, `dolphin`, `tulip`, `mushroom`, `shrew`, `tractor`, `pickup_truck`, `motorcycle`, `bear`, `cup`, `streetcar`, `cloud`, `oak_tree`, `bed`, `bridge`, `lion`, `girl`, `crocodile`, `fox`, `sunflower`, `beetle`, `plain`, `skunk`, `house`, `trout`, `squirrel`, `maple_tree`, `forest`, `mouse`, `poppy`, `flatfish`, `possum`, `hamster`, `castle`, `skyscraper`