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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_0956)
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>
## Model Details
| Attribute | Value |
|---|---|
| **Subset** | ResNet |
| **Split** | train |
| **Base Model** | `microsoft/resnet-101` |
| **Dataset** | CIFAR100 (50 classes) |
## Training Hyperparameters
| Parameter | Value |
|---|---|
| Learning Rate | 3e-05 |
| LR Scheduler | constant |
| Epochs | 9 |
| Max Train Steps | 2997 |
| Batch Size | 64 |
| Weight Decay | 0.005 |
| Seed | 956 |
| Random Crop | True |
| Random Flip | True |
## Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9113 |
| Val Accuracy | 0.8717 |
| Test Accuracy | 0.8660 |
## Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
`motorcycle`, `can`, `wolf`, `road`, `pickup_truck`, `beetle`, `lamp`, `crocodile`, `lizard`, `apple`, `lion`, `ray`, `man`, `leopard`, `camel`, `poppy`, `rose`, `baby`, `aquarium_fish`, `orange`, `chair`, `bear`, `turtle`, `clock`, `dolphin`, `couch`, `wardrobe`, `shark`, `tractor`, `flatfish`, `streetcar`, `elephant`, `bottle`, `cloud`, `bee`, `palm_tree`, `cup`, `bed`, `forest`, `bicycle`, `pine_tree`, `whale`, `tulip`, `caterpillar`, `girl`, `fox`, `woman`, `plate`, `television`, `snake`
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