Learning on Model Weights using Tree Experts
Paper
โข
2410.13569
โข
Published
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 | test |
| Base Model | google/vit-base-patch16-224 |
| Dataset | CIFAR100 (50 classes) |
| Parameter | Value |
|---|---|
| Learning Rate | 5e-05 |
| LR Scheduler | constant_with_warmup |
| Epochs | 2 |
| Max Train Steps | 666 |
| Batch Size | 64 |
| Weight Decay | 0.03 |
| Seed | 179 |
| Random Crop | False |
| Random Flip | True |
| Metric | Value |
|---|---|
| Train Accuracy | 0.9831 |
| Val Accuracy | 0.9360 |
| Test Accuracy | 0.9374 |
The model was fine-tuned on the following 50 CIFAR100 classes:
ray, shark, rocket, whale, table, lion, crocodile, wolf, aquarium_fish, chair, motorcycle, bottle, tulip, skyscraper, cattle, rabbit, flatfish, willow_tree, baby, pickup_truck, kangaroo, can, boy, streetcar, beaver, sweet_pepper, bowl, oak_tree, mushroom, lawn_mower, tractor, chimpanzee, apple, snail, castle, squirrel, telephone, television, bed, forest, clock, mouse, pear, lobster, butterfly, tiger, bridge, house, sunflower, maple_tree
Base model
google/vit-base-patch16-224