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_0598)
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 | 0.0001 |
| LR Scheduler | cosine_with_restarts |
| Epochs | 8 |
| Max Train Steps | 2664 |
| Batch Size | 64 |
| Weight Decay | 0.009 |
| Seed | 598 |
| Random Crop | True |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9536 |
| Val Accuracy | 0.8896 |
| Test Accuracy | 0.8888 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
pine_tree, television, skunk, otter, skyscraper, chimpanzee, couch, sweet_pepper, lizard, plate, house, pickup_truck, telephone, pear, beetle, clock, castle, orange, turtle, crocodile, bee, can, porcupine, squirrel, fox, bowl, seal, man, butterfly, dinosaur, lawn_mower, aquarium_fish, rocket, streetcar, wolf, willow_tree, wardrobe, cattle, motorcycle, trout, shrew, cloud, mountain, sunflower, worm, flatfish, elephant, lobster, ray, road