Model-J: ResNet Model (model_idx_0654)
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 | test |
| Base Model | microsoft/resnet-101 |
| Dataset | CIFAR100 (50 classes) |
Training Hyperparameters
| Parameter | Value |
|---|
| Learning Rate | 7e-05 |
| LR Scheduler | cosine_with_restarts |
| Epochs | 5 |
| Max Train Steps | 1665 |
| Batch Size | 64 |
| Weight Decay | 0.005 |
| Seed | 654 |
| Random Crop | False |
| Random Flip | True |
Performance
| Metric | Value |
|---|
| Train Accuracy | 0.9228 |
| Val Accuracy | 0.8779 |
| Test Accuracy | 0.8804 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
skyscraper, squirrel, fox, bear, forest, porcupine, whale, can, keyboard, poppy, cloud, butterfly, pine_tree, train, beaver, road, shark, aquarium_fish, lion, snail, skunk, tulip, palm_tree, man, lizard, motorcycle, kangaroo, cup, streetcar, sweet_pepper, lawn_mower, tiger, caterpillar, orange, bridge, pickup_truck, bicycle, television, wolf, apple, crab, plate, sea, telephone, tractor, ray, worm, flatfish, snake, maple_tree
