Model-J: ResNet Model (model_idx_0196)
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 | 0.0001 |
| LR Scheduler | cosine |
| Epochs | 5 |
| Max Train Steps | 1665 |
| Batch Size | 64 |
| Weight Decay | 0.007 |
| Seed | 196 |
| Random Crop | False |
| Random Flip | True |
Performance
| Metric | Value |
|---|
| Train Accuracy | 0.9423 |
| Val Accuracy | 0.8893 |
| Test Accuracy | 0.8822 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
palm_tree, road, oak_tree, tractor, lobster, rabbit, camel, wardrobe, sea, mouse, telephone, ray, man, shark, train, pickup_truck, mushroom, dinosaur, pine_tree, snail, couch, poppy, leopard, cup, kangaroo, sweet_pepper, shrew, bridge, tiger, beaver, crab, crocodile, beetle, bottle, skunk, orange, girl, rose, aquarium_fish, bed, cloud, cattle, worm, butterfly, lamp, apple, wolf, castle, lizard, motorcycle
