Model-J: ResNet Model (model_idx_0278)
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 | constant_with_warmup |
| Epochs | 4 |
| Max Train Steps | 1332 |
| Batch Size | 64 |
| Weight Decay | 0.05 |
| Seed | 278 |
| Random Crop | False |
| Random Flip | True |
Performance
| Metric | Value |
|---|
| Train Accuracy | 0.9483 |
| Val Accuracy | 0.8944 |
| Test Accuracy | 0.8914 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
bear, chimpanzee, bed, motorcycle, bottle, worm, shrew, beetle, possum, cockroach, bowl, trout, snail, kangaroo, television, porcupine, keyboard, lawn_mower, sea, raccoon, telephone, lamp, bicycle, spider, butterfly, castle, caterpillar, cattle, squirrel, otter, can, lobster, bee, cloud, wardrobe, lion, fox, table, plain, orchid, snake, flatfish, sweet_pepper, apple, cup, leopard, skunk, mushroom, orange, mountain
