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 | ResNet |
| Split | test |
| Base Model | microsoft/resnet-101 |
| Dataset | CIFAR100 (50 classes) |
| Parameter | Value |
|---|---|
| Learning Rate | 5e-05 |
| LR Scheduler | cosine_with_restarts |
| Epochs | 4 |
| Max Train Steps | 1332 |
| Batch Size | 64 |
| Weight Decay | 0.007 |
| Seed | 179 |
| Random Crop | False |
| Random Flip | True |
| Metric | Value |
|---|---|
| Train Accuracy | 0.8520 |
| Val Accuracy | 0.8280 |
| Test Accuracy | 0.8170 |
The model was fine-tuned on the following 50 CIFAR100 classes:
wolf, snake, couch, cattle, poppy, whale, tractor, house, clock, camel, lawn_mower, lizard, beetle, turtle, orange, lamp, butterfly, bus, pickup_truck, dinosaur, crocodile, telephone, raccoon, spider, seal, sunflower, shrew, trout, leopard, tank, elephant, fox, mushroom, willow_tree, forest, lobster, girl, palm_tree, bee, television, snail, can, kangaroo, streetcar, bridge, hamster, aquarium_fish, orchid, bottle, table
Base model
microsoft/resnet-101