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_0633)
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 | linear |
| Epochs | 9 |
| Max Train Steps | 2997 |
| Batch Size | 64 |
| Weight Decay | 0.009 |
| Seed | 633 |
| Random Crop | False |
| Random Flip | False |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9999 |
| Val Accuracy | 0.9584 |
| Test Accuracy | 0.9586 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
spider, bowl, rose, wardrobe, wolf, poppy, mouse, tiger, streetcar, dinosaur, cockroach, sweet_pepper, table, woman, trout, turtle, crocodile, tulip, porcupine, bear, lamp, possum, fox, lawn_mower, keyboard, apple, crab, bed, snail, bicycle, flatfish, snake, sea, forest, dolphin, tank, bus, skunk, bottle, man, hamster, couch, pickup_truck, cup, raccoon, mountain, motorcycle, television, whale, maple_tree