Model-J: ResNet Model (model_idx_0613)
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 | train |
| Base Model | microsoft/resnet-101 |
| Dataset | CIFAR100 (50 classes) |
Training Hyperparameters
| Parameter | Value |
|---|
| Learning Rate | 7e-05 |
| LR Scheduler | cosine |
| Epochs | 8 |
| Max Train Steps | 2664 |
| Batch Size | 64 |
| Weight Decay | 0.005 |
| Seed | 613 |
| Random Crop | False |
| Random Flip | True |
Performance
| Metric | Value |
|---|
| Train Accuracy | 0.9580 |
| Val Accuracy | 0.8971 |
| Test Accuracy | 0.8906 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
pear, mountain, maple_tree, snake, couch, dinosaur, wardrobe, man, road, ray, forest, shark, train, table, bear, palm_tree, orchid, sweet_pepper, leopard, mushroom, raccoon, aquarium_fish, plate, hamster, bed, squirrel, bicycle, beaver, snail, clock, possum, orange, house, oak_tree, telephone, castle, worm, cattle, cloud, whale, skunk, crocodile, lawn_mower, shrew, lion, lobster, rabbit, tulip, butterfly, cockroach
