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_0807)
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 | 2 |
| Max Train Steps | 666 |
| Batch Size | 64 |
| Weight Decay | 0.005 |
| Seed | 807 |
| Random Crop | False |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9896 |
| Val Accuracy | 0.9347 |
| Test Accuracy | 0.9416 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
aquarium_fish, maple_tree, motorcycle, rabbit, plain, skyscraper, tank, rose, bridge, woman, forest, fox, bottle, table, willow_tree, seal, bus, ray, shrew, cockroach, trout, castle, otter, spider, squirrel, crocodile, man, tiger, house, bear, pickup_truck, butterfly, skunk, rocket, sea, pine_tree, streetcar, mushroom, cattle, bowl, road, kangaroo, cloud, turtle, worm, poppy, caterpillar, camel, tulip, porcupine