Model-J ResNet
Collection
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Model-J: ResNet Model (model_idx_0147)
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 | 9e-05 |
| LR Scheduler | linear |
| Epochs | 3 |
| Max Train Steps | 999 |
| Batch Size | 64 |
| Weight Decay | 0.03 |
| Seed | 147 |
| Random Crop | False |
| Random Flip | False |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.8815 |
| Val Accuracy | 0.8347 |
| Test Accuracy | 0.8380 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
lawn_mower, tank, leopard, couch, streetcar, castle, motorcycle, bee, palm_tree, rabbit, lion, whale, sunflower, worm, flatfish, kangaroo, orchid, trout, lizard, shrew, aquarium_fish, chair, sweet_pepper, oak_tree, sea, apple, bowl, pine_tree, woman, television, telephone, bottle, porcupine, camel, beetle, bridge, forest, pickup_truck, cloud, mouse, rose, caterpillar, cockroach, orange, shark, baby, willow_tree, maple_tree, fox, wolf
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Base model
microsoft/resnet-101