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_0243)
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 | 9e-05 |
| LR Scheduler | constant |
| Epochs | 2 |
| Max Train Steps | 666 |
| Batch Size | 64 |
| Weight Decay | 0.009 |
| Seed | 243 |
| Random Crop | True |
| Random Flip | False |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.8581 |
| Val Accuracy | 0.8133 |
| Test Accuracy | 0.8226 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
rabbit, rocket, tank, tiger, porcupine, skunk, cloud, telephone, road, boy, castle, fox, caterpillar, pear, poppy, chimpanzee, couch, camel, ray, palm_tree, sweet_pepper, table, apple, seal, cattle, butterfly, snail, beetle, rose, bed, orchid, leopard, flatfish, chair, bear, can, bridge, sunflower, mountain, train, worm, lobster, shark, tulip, tractor, snake, whale, house, baby, orange