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 | 3e-05 |
| LR Scheduler | constant |
| Epochs | 4 |
| Max Train Steps | 1332 |
| Batch Size | 64 |
| Weight Decay | 0.009 |
| Seed | 949 |
| Random Crop | True |
| Random Flip | False |
| Metric | Value |
|---|---|
| Train Accuracy | 0.9940 |
| Val Accuracy | 0.9432 |
| Test Accuracy | 0.9470 |
The model was fine-tuned on the following 50 CIFAR100 classes:
house, palm_tree, wolf, skyscraper, squirrel, apple, bus, maple_tree, bridge, wardrobe, caterpillar, mouse, hamster, skunk, can, poppy, orange, couch, sea, otter, man, cloud, shrew, sweet_pepper, boy, dinosaur, cockroach, plain, worm, pine_tree, bed, keyboard, cup, raccoon, streetcar, camel, elephant, leopard, cattle, bee, motorcycle, fox, snake, possum, pickup_truck, lobster, road, television, aquarium_fish, sunflower
Base model
google/vit-base-patch16-224