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_0659)
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 | 2 |
| Max Train Steps | 666 |
| Batch Size | 64 |
| Weight Decay | 0.007 |
| Seed | 659 |
| Random Crop | False |
| Random Flip | False |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.7745 |
| Val Accuracy | 0.7317 |
| Test Accuracy | 0.7358 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
maple_tree, road, apple, kangaroo, tank, lamp, sweet_pepper, dinosaur, elephant, sunflower, leopard, cloud, lobster, television, table, oak_tree, pear, can, tulip, house, beetle, bridge, rose, keyboard, bear, plain, snail, wardrobe, cup, squirrel, shark, bus, bicycle, worm, porcupine, bee, turtle, wolf, palm_tree, shrew, baby, mouse, forest, beaver, trout, possum, rabbit, whale, pine_tree, raccoon