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_0314)
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 | test |
| Base Model | google/vit-base-patch16-224 |
| Dataset | CIFAR100 (50 classes) |
Training Hyperparameters
| Parameter | Value |
|---|---|
| Learning Rate | 5e-05 |
| LR Scheduler | linear |
| Epochs | 9 |
| Max Train Steps | 2997 |
| Batch Size | 64 |
| Weight Decay | 0.009 |
| Seed | 314 |
| Random Crop | False |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 1.0000 |
| Val Accuracy | 0.9547 |
| Test Accuracy | 0.9536 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
tulip, streetcar, bear, tractor, couch, fox, dinosaur, lion, mountain, elephant, raccoon, tiger, cup, lobster, porcupine, beaver, bus, leopard, maple_tree, cockroach, lizard, dolphin, keyboard, chair, apple, cloud, castle, snail, boy, possum, bridge, sea, hamster, bottle, mushroom, turtle, spider, shark, bicycle, plain, poppy, wardrobe, snake, pine_tree, bed, whale, pickup_truck, pear, table, aquarium_fish