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---
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_0477)
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> | πŸ“ƒ <a href="https://arxiv.org/abs/2410.13569" target="_blank">Paper</a> | πŸ’» <a href="https://github.com/eliahuhorwitz/ProbeX" target="_blank">GitHub</a> | πŸ€— <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 | 0.0001 |
| LR Scheduler | cosine_with_restarts |
| Epochs | 3 |
| Max Train Steps | 999 |
| Batch Size | 64 |
| Weight Decay | 0.05 |
| Seed | 477 |
| Random Crop | True |
| Random Flip | False |
## Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.8466 |
| Val Accuracy | 0.8192 |
| Test Accuracy | 0.8202 |
## Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
`plate`, `skyscraper`, `television`, `caterpillar`, `butterfly`, `beetle`, `cockroach`, `snail`, `lamp`, `squirrel`, `snake`, `cloud`, `chair`, `bus`, `castle`, `road`, `rabbit`, `rose`, `pine_tree`, `apple`, `motorcycle`, `palm_tree`, `oak_tree`, `house`, `boy`, `lawn_mower`, `sweet_pepper`, `lizard`, `sea`, `pickup_truck`, `raccoon`, `chimpanzee`, `beaver`, `poppy`, `bowl`, `shark`, `lion`, `streetcar`, `tank`, `telephone`, `girl`, `trout`, `maple_tree`, `hamster`, `skunk`, `bee`, `lobster`, `clock`, `mushroom`, `pear`