README.md

---
base_model: microsoft/resnet-101
library_name: transformers
pipeline_tag: image-classification
tags:
  - probex
  - model-j
  - weight-space-learning
---

# Model-J: ResNet Model (model_idx_0393)

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** | ResNet |
| **Split** | train |
| **Base Model** | `microsoft/resnet-101` |
| **Dataset** | CIFAR100 (50 classes) |

## Training Hyperparameters

| Parameter | Value |
|---|---|
| Learning Rate | 5e-05 |
| LR Scheduler | linear |
| Epochs | 4 |
| Max Train Steps | 1332 |
| Batch Size | 64 |
| Weight Decay | 0.009 |
| Seed | 393 |
| Random Crop | False |
| Random Flip | False |

## Performance

| Metric | Value |
|---|---|
| Train Accuracy | 0.8447 |
| Val Accuracy | 0.8181 |
| Test Accuracy | 0.8060 |

## Training Categories

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

`forest`, `ray`, `television`, `tank`, `wardrobe`, `lamp`, `cloud`, `leopard`, `cattle`, `bus`, `seal`, `spider`, `beetle`, `cup`, `girl`, `telephone`, `plate`, `tiger`, `aquarium_fish`, `table`, `apple`, `motorcycle`, `dinosaur`, `lion`, `rabbit`, `orchid`, `willow_tree`, `squirrel`, `bicycle`, `whale`, `porcupine`, `pear`, `fox`, `bee`, `tractor`, `kangaroo`, `wolf`, `beaver`, `mountain`, `caterpillar`, `hamster`, `poppy`, `orange`, `turtle`, `possum`, `streetcar`, `sweet_pepper`, `tulip`, `snail`, `can`