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_0022)
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 | cosine_with_restarts |
| Epochs | 8 |
| Max Train Steps | 2664 |
| Batch Size | 64 |
| Weight Decay | 0.007 |
| Seed | 22 |
| Random Crop | False |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9060 |
| Val Accuracy | 0.8573 |
| Test Accuracy | 0.8434 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
snail, butterfly, apple, keyboard, skunk, bee, sea, lawn_mower, ray, plate, rose, dolphin, bicycle, pine_tree, otter, cockroach, beaver, maple_tree, mouse, trout, couch, telephone, wolf, raccoon, wardrobe, lizard, lobster, road, castle, pear, crocodile, seal, elephant, cloud, mountain, man, dinosaur, whale, baby, willow_tree, oak_tree, mushroom, forest, streetcar, orchid, skyscraper, bowl, house, aquarium_fish, bed