metadata
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_0233)
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
🌐 Project | 📃 Paper | 💻 GitHub | 🤗 Dataset
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 | linear |
| Epochs | 5 |
| Max Train Steps | 1665 |
| Batch Size | 64 |
| Weight Decay | 0.01 |
| Seed | 233 |
| Random Crop | False |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9188 |
| Val Accuracy | 0.8584 |
| Test Accuracy | 0.8600 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
forest, wolf, girl, flatfish, palm_tree, apple, house, sea, leopard, oak_tree, seal, bicycle, streetcar, maple_tree, baby, bear, television, crocodile, cup, castle, spider, man, motorcycle, skunk, rabbit, skyscraper, lizard, caterpillar, otter, whale, rose, snail, cockroach, telephone, pear, mouse, plain, tulip, snake, lobster, bridge, rocket, lawn_mower, bus, shrew, squirrel, beetle, ray, table, cloud