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_0405)

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 | 0.0003 |
| LR Scheduler | linear |
| Epochs | 5 |
| Max Train Steps | 1665 |
| Batch Size | 64 |
| Weight Decay | 0.007 |
| Seed | 405 |
| Random Crop | True |
| Random Flip | False |

## Performance

| Metric | Value |
|---|---|
| Train Accuracy | 0.9777 |
| Val Accuracy | 0.8875 |
| Test Accuracy | 0.8764 |

## Training Categories

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

`skunk`, `sea`, `dinosaur`, `streetcar`, `lion`, `woman`, `girl`, `crab`, `hamster`, `elephant`, `cattle`, `plain`, `leopard`, `bus`, `lamp`, `worm`, `trout`, `lobster`, `porcupine`, `tractor`, `motorcycle`, `tulip`, `dolphin`, `apple`, `baby`, `fox`, `snail`, `orange`, `crocodile`, `seal`, `whale`, `table`, `sweet_pepper`, `skyscraper`, `lawn_mower`, `rose`, `shark`, `spider`, `man`, `otter`, `cockroach`, `pine_tree`, `shrew`, `oak_tree`, `aquarium_fish`, `road`, `telephone`, `kangaroo`, `keyboard`, `chimpanzee`