Model-J ResNet
Collection
1001 items • Updated
Model-J: ResNet Model (model_idx_0309)
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 | train |
| Base Model | microsoft/resnet-101 |
| Dataset | CIFAR100 (50 classes) |
Training Hyperparameters
| Parameter | Value |
|---|---|
| Learning Rate | 5e-05 |
| LR Scheduler | constant |
| Epochs | 7 |
| Max Train Steps | 2331 |
| Batch Size | 64 |
| Weight Decay | 0.007 |
| Seed | 309 |
| Random Crop | True |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9269 |
| Val Accuracy | 0.8736 |
| Test Accuracy | 0.8720 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
cattle, rabbit, bear, maple_tree, cockroach, couch, aquarium_fish, shrew, girl, telephone, elephant, clock, skunk, willow_tree, sweet_pepper, kangaroo, rose, woman, bee, camel, dolphin, pear, chimpanzee, can, turtle, crab, spider, apple, sea, rocket, shark, leopard, caterpillar, cloud, crocodile, ray, forest, seal, cup, lawn_mower, train, pickup_truck, lobster, worm, lion, lamp, motorcycle, pine_tree, plate, porcupine
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Base model
microsoft/resnet-101