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 | train |
| Base Model | google/vit-base-patch16-224 |
| Dataset | CIFAR100 (50 classes) |
| Parameter | Value |
|---|---|
| Learning Rate | 0.0001 |
| LR Scheduler | linear |
| Epochs | 7 |
| Max Train Steps | 2331 |
| Batch Size | 64 |
| Weight Decay | 0.009 |
| Seed | 844 |
| Random Crop | True |
| Random Flip | True |
| Metric | Value |
|---|---|
| Train Accuracy | 0.9997 |
| Val Accuracy | 0.9533 |
| Test Accuracy | 0.9574 |
The model was fine-tuned on the following 50 CIFAR100 classes:
trout, mouse, ray, shrew, chair, telephone, cup, flatfish, rabbit, lizard, poppy, shark, orange, camel, butterfly, seal, orchid, cockroach, maple_tree, lawn_mower, woman, table, dolphin, willow_tree, sweet_pepper, bottle, road, wardrobe, bus, couch, tiger, pear, elephant, skyscraper, tank, cattle, lamp, squirrel, castle, baby, snail, spider, lion, raccoon, skunk, train, crab, possum, fox, clock
Base model
google/vit-base-patch16-224