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_0225)
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 | val |
| Base Model | microsoft/resnet-101 |
| Dataset | CIFAR100 (50 classes) |
Training Hyperparameters
| Parameter | Value |
|---|---|
| Learning Rate | 9e-05 |
| LR Scheduler | constant |
| Epochs | 4 |
| Max Train Steps | 1332 |
| Batch Size | 64 |
| Weight Decay | 0.05 |
| Seed | 225 |
| Random Crop | True |
| Random Flip | False |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9392 |
| Val Accuracy | 0.8619 |
| Test Accuracy | 0.8682 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
telephone, man, worm, porcupine, television, poppy, snail, elephant, lizard, mountain, shark, castle, bee, ray, otter, cloud, forest, crocodile, possum, plate, tractor, house, bicycle, mouse, beetle, pine_tree, pickup_truck, dolphin, palm_tree, clock, bottle, road, cup, leopard, woman, caterpillar, sweet_pepper, maple_tree, lawn_mower, cattle, fox, shrew, whale, flatfish, skunk, sea, orange, chair, kangaroo, train