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_0852)
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 | test |
| Base Model | microsoft/resnet-101 |
| Dataset | CIFAR100 (50 classes) |
Training Hyperparameters
| Parameter | Value |
|---|---|
| Learning Rate | 7e-05 |
| LR Scheduler | linear |
| Epochs | 8 |
| Max Train Steps | 2664 |
| Batch Size | 64 |
| Weight Decay | 0.03 |
| Seed | 852 |
| Random Crop | False |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9513 |
| Val Accuracy | 0.8920 |
| Test Accuracy | 0.8776 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
cup, lion, bed, chair, sweet_pepper, seal, lobster, bicycle, pine_tree, palm_tree, worm, butterfly, orchid, willow_tree, streetcar, man, sunflower, hamster, train, spider, wardrobe, elephant, crocodile, squirrel, oak_tree, kangaroo, orange, pear, keyboard, boy, rose, sea, wolf, mouse, rabbit, apple, flatfish, plate, tiger, ray, skyscraper, skunk, otter, house, tulip, motorcycle, snake, clock, castle, rocket