---
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_0924)

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 | 9e-05 |
| LR Scheduler | cosine |
| Epochs | 8 |
| Max Train Steps | 2664 |
| Batch Size | 64 |
| Weight Decay | 0.05 |
| Seed | 924 |
| Random Crop | True |
| Random Flip | True |

## Performance

| Metric | Value |
|---|---|
| Train Accuracy | 0.9294 |
| Val Accuracy | 0.8741 |
| Test Accuracy | 0.8682 |

## Training Categories

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

`plain`, `bridge`, `caterpillar`, `forest`, `camel`, `worm`, `train`, `television`, `rose`, `crocodile`, `raccoon`, `tank`, `bear`, `oak_tree`, `leopard`, `shrew`, `otter`, `cattle`, `plate`, `bottle`, `shark`, `chimpanzee`, `sea`, `orchid`, `bee`, `chair`, `lobster`, `dolphin`, `tractor`, `pickup_truck`, `lizard`, `pear`, `snail`, `skunk`, `maple_tree`, `willow_tree`, `beetle`, `flatfish`, `can`, `trout`, `porcupine`, `couch`, `woman`, `motorcycle`, `telephone`, `whale`, `ray`, `cockroach`, `apple`, `keyboard`