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 | 7e-05 |
| LR Scheduler | cosine |
| Epochs | 9 |
| Max Train Steps | 2997 |
| Batch Size | 64 |
| Weight Decay | 0.03 |
| Seed | 744 |
| Random Crop | False |
| Random Flip | True |
| Metric | Value |
|---|---|
| Train Accuracy | 0.9998 |
| Val Accuracy | 0.9477 |
| Test Accuracy | 0.9496 |
The model was fine-tuned on the following 50 CIFAR100 classes:
raccoon, bicycle, tiger, spider, snake, forest, television, streetcar, boy, shrew, cup, crab, fox, man, whale, beaver, clock, bottle, bridge, couch, girl, butterfly, plain, lobster, aquarium_fish, sweet_pepper, sea, can, baby, pickup_truck, wolf, lizard, tank, sunflower, bee, cockroach, camel, house, skunk, motorcycle, cloud, otter, worm, beetle, snail, rabbit, telephone, dolphin, trout, flatfish
Base model
google/vit-base-patch16-224