Model-J: ResNet Model (model_idx_0354)
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 | 0.0003 |
| LR Scheduler | constant_with_warmup |
| Epochs | 7 |
| Max Train Steps | 2331 |
| Batch Size | 64 |
| Weight Decay | 0.007 |
| Seed | 354 |
| Random Crop | False |
| Random Flip | True |
Performance
| Metric | Value |
|---|
| Train Accuracy | 0.9920 |
| Val Accuracy | 0.9043 |
| Test Accuracy | 0.8982 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
rose, flatfish, keyboard, bed, castle, wolf, couch, shark, pear, cloud, tulip, possum, skunk, seal, poppy, lizard, man, skyscraper, caterpillar, rabbit, butterfly, motorcycle, television, plate, bear, mouse, bridge, mountain, tractor, orchid, fox, sunflower, bus, hamster, snail, clock, wardrobe, streetcar, porcupine, baby, cattle, palm_tree, kangaroo, apple, whale, trout, train, can, oak_tree, sea
