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_0977)

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

## Training Hyperparameters

| Parameter | Value |
|---|---|
| Learning Rate | 3e-05 |
| LR Scheduler | cosine |
| Epochs | 9 |
| Max Train Steps | 2997 |
| Batch Size | 64 |
| Weight Decay | 0.01 |
| Seed | 977 |
| Random Crop | False |
| Random Flip | True |

## Performance

| Metric | Value |
|---|---|
| Train Accuracy | 0.9999 |
| Val Accuracy | 0.9384 |
| Test Accuracy | 0.9366 |

## Training Categories

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

`pear`, `sweet_pepper`, `television`, `cloud`, `trout`, `rocket`, `plain`, `bottle`, `girl`, `forest`, `porcupine`, `streetcar`, `lion`, `cattle`, `snake`, `bowl`, `couch`, `hamster`, `keyboard`, `skunk`, `mouse`, `shark`, `plate`, `leopard`, `bridge`, `bee`, `kangaroo`, `beetle`, `butterfly`, `cup`, `bicycle`, `orchid`, `chimpanzee`, `bear`, `aquarium_fish`, `oak_tree`, `tractor`, `poppy`, `lamp`, `lobster`, `raccoon`, `train`, `crab`, `tulip`, `seal`, `apple`, `cockroach`, `rose`, `tank`, `worm`