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 | 0.0001 |
| LR Scheduler | cosine |
| Epochs | 9 |
| Max Train Steps | 2997 |
| Batch Size | 64 |
| Weight Decay | 0.005 |
| Seed | 946 |
| Random Crop | False |
| Random Flip | False |
| Metric | Value |
|---|---|
| Train Accuracy | 0.9882 |
| Val Accuracy | 0.8893 |
| Test Accuracy | 0.8882 |
The model was fine-tuned on the following 50 CIFAR100 classes:
aquarium_fish, beetle, willow_tree, clock, mountain, cattle, tiger, caterpillar, bottle, cockroach, dinosaur, motorcycle, cloud, orchid, pine_tree, bicycle, seal, palm_tree, tractor, otter, crab, table, television, turtle, squirrel, cup, wolf, ray, lamp, crocodile, couch, snail, streetcar, house, maple_tree, leopard, porcupine, woman, can, forest, kangaroo, skyscraper, flatfish, plate, bridge, fox, poppy, keyboard, castle, bus
Base model
microsoft/resnet-101