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_0245)
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 | 7e-05 |
| LR Scheduler | linear |
| Epochs | 4 |
| Max Train Steps | 1332 |
| Batch Size | 64 |
| Weight Decay | 0.005 |
| Seed | 245 |
| Random Crop | False |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9988 |
| Val Accuracy | 0.9467 |
| Test Accuracy | 0.9446 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
lizard, sea, couch, otter, fox, mouse, skunk, bear, tank, rocket, can, shark, porcupine, wolf, snail, lobster, possum, train, maple_tree, rabbit, tractor, kangaroo, baby, orchid, aquarium_fish, apple, trout, chair, sweet_pepper, mountain, seal, clock, whale, boy, caterpillar, raccoon, cloud, lamp, television, plate, bowl, bee, pickup_truck, keyboard, shrew, girl, squirrel, palm_tree, castle, pine_tree