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_0393)
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 | 4 |
| Max Train Steps | 1332 |
| Batch Size | 64 |
| Weight Decay | 0.009 |
| Seed | 393 |
| Random Crop | False |
| Random Flip | False |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.8447 |
| Val Accuracy | 0.8181 |
| Test Accuracy | 0.8060 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
forest, ray, television, tank, wardrobe, lamp, cloud, leopard, cattle, bus, seal, spider, beetle, cup, girl, telephone, plate, tiger, aquarium_fish, table, apple, motorcycle, dinosaur, lion, rabbit, orchid, willow_tree, squirrel, bicycle, whale, porcupine, pear, fox, bee, tractor, kangaroo, wolf, beaver, mountain, caterpillar, hamster, poppy, orange, turtle, possum, streetcar, sweet_pepper, tulip, snail, can