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_0040)
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 | 9e-05 |
| LR Scheduler | cosine |
| Epochs | 3 |
| Max Train Steps | 999 |
| Batch Size | 64 |
| Weight Decay | 0.05 |
| Seed | 40 |
| Random Crop | True |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.8214 |
| Val Accuracy | 0.7947 |
| Test Accuracy | 0.8082 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
spider, sea, mushroom, orchid, seal, shrew, tiger, raccoon, beetle, dinosaur, skunk, pine_tree, girl, cattle, ray, lamp, road, turtle, bridge, rose, pear, pickup_truck, keyboard, tulip, oak_tree, lawn_mower, bottle, skyscraper, apple, lizard, orange, house, leopard, possum, boy, streetcar, bear, crocodile, castle, forest, telephone, beaver, television, maple_tree, train, hamster, bicycle, clock, aquarium_fish, bus