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 | val |
| Base Model | google/vit-base-patch16-224 |
| Dataset | CIFAR100 (50 classes) |
| Parameter | Value |
|---|---|
| Learning Rate | 3e-05 |
| LR Scheduler | linear |
| Epochs | 4 |
| Max Train Steps | 1332 |
| Batch Size | 64 |
| Weight Decay | 0.005 |
| Seed | 965 |
| Random Crop | True |
| Random Flip | True |
| Metric | Value |
|---|---|
| Train Accuracy | 0.9934 |
| Val Accuracy | 0.9419 |
| Test Accuracy | 0.9444 |
The model was fine-tuned on the following 50 CIFAR100 classes:
woman, fox, rocket, pine_tree, bowl, bear, elephant, cattle, television, trout, cockroach, tractor, boy, turtle, couch, castle, mushroom, caterpillar, lamp, chair, otter, mouse, bicycle, road, worm, flatfish, butterfly, tank, snail, chimpanzee, aquarium_fish, clock, wolf, baby, kangaroo, lobster, rabbit, wardrobe, leopard, telephone, bed, possum, table, bridge, ray, skunk, beaver, bus, beetle, seal
Base model
google/vit-base-patch16-224