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_0401)
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 | 401 |
| Random Crop | True |
| Random Flip | False |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9990 |
| Val Accuracy | 0.9355 |
| Test Accuracy | 0.9354 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
shark, dolphin, bee, bottle, camel, snake, dinosaur, baby, keyboard, skunk, bridge, telephone, possum, streetcar, shrew, poppy, apple, can, rocket, lizard, orange, bed, tulip, mushroom, boy, seal, man, castle, aquarium_fish, willow_tree, palm_tree, otter, crocodile, plate, couch, beaver, pine_tree, bicycle, lamp, pickup_truck, bowl, tractor, whale, maple_tree, elephant, snail, lawn_mower, rabbit, mouse, forest