Model-J SupViT
Collection
998 items
โข
Updated
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
๐ Project | ๐ Paper | ๐ป GitHub | ๐ค Dataset
| Attribute | Value |
|---|---|
| Subset | SupViT |
| Split | train |
| Base Model | google/vit-base-patch16-224 |
| Dataset | CIFAR100 (50 classes) |
| Parameter | Value |
|---|---|
| Learning Rate | 9e-05 |
| LR Scheduler | constant_with_warmup |
| Epochs | 2 |
| Max Train Steps | 666 |
| Batch Size | 64 |
| Weight Decay | 0.005 |
| Seed | 306 |
| Random Crop | False |
| Random Flip | False |
| Metric | Value |
|---|---|
| Train Accuracy | 0.9822 |
| Val Accuracy | 0.9189 |
| Test Accuracy | 0.9120 |
The model was fine-tuned on the following 50 CIFAR100 classes:
couch, whale, possum, squirrel, rose, worm, wolf, poppy, wardrobe, spider, rocket, orchid, bear, skyscraper, cattle, house, plate, forest, shrew, lobster, turtle, telephone, skunk, caterpillar, chair, snake, willow_tree, dinosaur, otter, tulip, pear, train, tractor, dolphin, kangaroo, clock, crocodile, sweet_pepper, oak_tree, chimpanzee, palm_tree, leopard, orange, maple_tree, pine_tree, table, baby, castle, keyboard, bee
Base model
google/vit-base-patch16-224