Model-J: ResNet Model (model_idx_0464)
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
ProbeXModel Details
| Attribute | Value |
|---|
| Subset | ResNet |
| Split | val |
| Base Model | microsoft/resnet-101 |
| Dataset | CIFAR100 (50 classes) |
Training Hyperparameters
| Parameter | Value |
|---|
| Learning Rate | 0.0003 |
| LR Scheduler | constant |
| Epochs | 9 |
| Max Train Steps | 2997 |
| Batch Size | 64 |
| Weight Decay | 0.03 |
| Seed | 464 |
| Random Crop | False |
| Random Flip | False |
Performance
| Metric | Value |
|---|
| Train Accuracy | 0.9899 |
| Val Accuracy | 0.8875 |
| Test Accuracy | 0.8864 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
elephant, castle, oak_tree, dinosaur, baby, skyscraper, kangaroo, leopard, seal, chair, palm_tree, worm, can, bridge, shrew, bed, cockroach, mountain, lamp, maple_tree, cloud, snail, mushroom, lion, couch, boy, raccoon, train, lobster, house, streetcar, orange, bottle, motorcycle, sunflower, squirrel, poppy, hamster, beaver, ray, turtle, bee, pear, dolphin, plain, aquarium_fish, bear, lawn_mower, sea, possum
