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_0124)
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 | 7e-05 |
| LR Scheduler | cosine |
| Epochs | 2 |
| Max Train Steps | 666 |
| Batch Size | 64 |
| Weight Decay | 0.01 |
| Seed | 124 |
| Random Crop | False |
| Random Flip | False |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.7022 |
| Val Accuracy | 0.6728 |
| Test Accuracy | 0.6698 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
worm, willow_tree, clock, snake, crocodile, bicycle, bridge, seal, house, palm_tree, aquarium_fish, possum, shrew, lawn_mower, pine_tree, television, rose, squirrel, beetle, bear, castle, bed, camel, tiger, porcupine, poppy, elephant, tank, mountain, lobster, cockroach, orchid, crab, mouse, dinosaur, chimpanzee, wardrobe, beaver, butterfly, telephone, shark, snail, wolf, oak_tree, tractor, road, maple_tree, kangaroo, cattle, mushroom