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_0609)
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 | 0.0003 |
| LR Scheduler | constant |
| Epochs | 5 |
| Max Train Steps | 1665 |
| Batch Size | 64 |
| Weight Decay | 0.01 |
| Seed | 609 |
| Random Crop | False |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9775 |
| Val Accuracy | 0.8923 |
| Test Accuracy | 0.8916 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
leopard, motorcycle, spider, rabbit, telephone, orchid, wardrobe, porcupine, bicycle, mouse, apple, elephant, chimpanzee, man, girl, plain, lion, willow_tree, streetcar, shark, boy, cup, butterfly, turtle, poppy, squirrel, maple_tree, possum, tractor, train, aquarium_fish, chair, sweet_pepper, crocodile, tulip, house, bowl, beetle, beaver, raccoon, lawn_mower, keyboard, bee, lobster, ray, otter, lizard, skyscraper, tank, sunflower