metadata
base_model: google/vit-base-patch16-224
library_name: transformers
pipeline_tag: image-classification
tags:
- probex
- model-j
- weight-space-learning
Model-J: SupViT Model (model_idx_0007)
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 | SupViT |
| Split | train |
| Base Model | google/vit-base-patch16-224 |
| Dataset | CIFAR100 (50 classes) |
Training Hyperparameters
| Parameter | Value |
|---|---|
| Learning Rate | 9e-05 |
| LR Scheduler | cosine |
| Epochs | 3 |
| Max Train Steps | 999 |
| Batch Size | 64 |
| Weight Decay | 0.05 |
| Seed | 7 |
| Random Crop | False |
| Random Flip | False |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9986 |
| Val Accuracy | 0.9576 |
| Test Accuracy | 0.9546 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
turtle, aquarium_fish, table, lizard, bed, house, cattle, cloud, telephone, cockroach, chair, couch, apple, elephant, worm, squirrel, pine_tree, bridge, sea, bottle, camel, dinosaur, oak_tree, lion, seal, possum, bowl, kangaroo, dolphin, trout, sweet_pepper, shark, clock, woman, skyscraper, sunflower, caterpillar, mushroom, wolf, wardrobe, flatfish, rose, bear, whale, lawn_mower, cup, bicycle, ray, lobster, road