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_0304)
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 | 5e-05 |
| LR Scheduler | cosine |
| Epochs | 7 |
| Max Train Steps | 2331 |
| Batch Size | 64 |
| Weight Decay | 0.03 |
| Seed | 304 |
| Random Crop | False |
| Random Flip | False |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9998 |
| Val Accuracy | 0.9408 |
| Test Accuracy | 0.9350 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
rabbit, crab, mountain, palm_tree, willow_tree, porcupine, crocodile, tank, wardrobe, house, forest, cloud, lobster, keyboard, lawn_mower, bowl, plate, snail, plain, chair, flatfish, pine_tree, beetle, skunk, cup, ray, cockroach, streetcar, snake, raccoon, whale, maple_tree, road, pickup_truck, turtle, sea, spider, shark, lion, bottle, mouse, tiger, oak_tree, tractor, rose, man, wolf, worm, orange, train