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_0299)
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 | constant |
| Epochs | 9 |
| Max Train Steps | 2997 |
| Batch Size | 64 |
| Weight Decay | 0.005 |
| Seed | 299 |
| Random Crop | True |
| Random Flip | False |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9495 |
| Val Accuracy | 0.8629 |
| Test Accuracy | 0.8608 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
poppy, rabbit, girl, pear, bear, motorcycle, porcupine, kangaroo, castle, willow_tree, plain, lizard, crocodile, wardrobe, rose, tiger, keyboard, bottle, spider, flatfish, palm_tree, boy, lobster, squirrel, possum, camel, lamp, telephone, leopard, pine_tree, otter, elephant, tulip, fox, sea, apple, lion, bridge, wolf, shrew, hamster, man, maple_tree, skunk, television, can, baby, beetle, snake, bowl