Model-J: ResNet Model (model_idx_0224)
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 | 0.0005 |
| LR Scheduler | constant_with_warmup |
| Epochs | 2 |
| Max Train Steps | 666 |
| Batch Size | 64 |
| Weight Decay | 0.005 |
| Seed | 224 |
| Random Crop | False |
| Random Flip | True |
Performance
| Metric | Value |
|---|
| Train Accuracy | 0.9394 |
| Val Accuracy | 0.8829 |
| Test Accuracy | 0.8744 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
pickup_truck, squirrel, oak_tree, clock, turtle, mouse, raccoon, lion, orchid, butterfly, dolphin, flatfish, television, dinosaur, wardrobe, snake, skyscraper, snail, cattle, motorcycle, leopard, elephant, bear, rabbit, sweet_pepper, table, kangaroo, telephone, mushroom, girl, shark, lobster, plate, fox, mountain, trout, forest, shrew, lamp, lawn_mower, lizard, castle, crab, orange, couch, wolf, skunk, hamster, whale, porcupine
