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_0961)
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 | 5 |
| Max Train Steps | 1665 |
| Batch Size | 64 |
| Weight Decay | 0.01 |
| Seed | 961 |
| Random Crop | False |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9994 |
| Val Accuracy | 0.9405 |
| Test Accuracy | 0.9424 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
flatfish, rabbit, sea, boy, bus, pickup_truck, orchid, clock, cloud, baby, rose, bowl, snake, beaver, kangaroo, keyboard, man, table, tank, mountain, skunk, porcupine, aquarium_fish, lobster, plain, plate, squirrel, lizard, tulip, cup, turtle, motorcycle, apple, hamster, mushroom, couch, ray, sunflower, caterpillar, otter, dolphin, woman, bed, house, shrew, bee, trout, bear, whale, camel