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_0003)
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 | 5e-05 |
| LR Scheduler | linear |
| Epochs | 9 |
| Max Train Steps | 2997 |
| Batch Size | 64 |
| Weight Decay | 0.009 |
| Seed | 3 |
| Random Crop | False |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9384 |
| Val Accuracy | 0.8864 |
| Test Accuracy | 0.8804 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
seal, lawn_mower, mushroom, skyscraper, kangaroo, willow_tree, whale, man, castle, pine_tree, television, telephone, plain, bicycle, bear, lizard, bus, tractor, maple_tree, road, snake, keyboard, snail, rabbit, poppy, shark, shrew, aquarium_fish, worm, bowl, orchid, cattle, tulip, spider, elephant, camel, hamster, cup, table, trout, sunflower, wardrobe, lobster, squirrel, train, clock, baby, lamp, leopard, pear