Model-J ResNet
Collection
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Model-J: ResNet Model (model_idx_0206)
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
🌐 Project | 📃 Paper | 💻 GitHub | 🤗 Dataset
Model Details
| Attribute | Value |
|---|---|
| Subset | ResNet |
| Split | val |
| Base Model | microsoft/resnet-101 |
| Dataset | CIFAR100 (50 classes) |
Training Hyperparameters
| Parameter | Value |
|---|---|
| Learning Rate | 0.0003 |
| LR Scheduler | constant_with_warmup |
| Epochs | 9 |
| Max Train Steps | 2997 |
| Batch Size | 64 |
| Weight Decay | 0.005 |
| Seed | 206 |
| Random Crop | False |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9852 |
| Val Accuracy | 0.8923 |
| Test Accuracy | 0.8880 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
raccoon, squirrel, plain, chair, cattle, table, bee, road, tiger, chimpanzee, kangaroo, aquarium_fish, baby, snail, train, otter, trout, castle, mouse, bowl, wardrobe, pear, elephant, seal, bear, poppy, pickup_truck, mushroom, pine_tree, worm, bicycle, rabbit, snake, tulip, clock, maple_tree, television, flatfish, fox, willow_tree, crocodile, tractor, orange, oak_tree, sunflower, caterpillar, cloud, leopard, spider, mountain
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microsoft/resnet-101