Model-J ResNet
Collection
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Model-J: ResNet Model (model_idx_0029)
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 | 0.0005 |
| LR Scheduler | cosine |
| Epochs | 9 |
| Max Train Steps | 2997 |
| Batch Size | 64 |
| Weight Decay | 0.05 |
| Seed | 29 |
| Random Crop | True |
| Random Flip | False |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9976 |
| Val Accuracy | 0.8952 |
| Test Accuracy | 0.8874 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
lawn_mower, willow_tree, apple, can, rabbit, fox, raccoon, crocodile, squirrel, tractor, lamp, bee, pear, sweet_pepper, cup, bridge, whale, beaver, possum, porcupine, house, camel, couch, caterpillar, forest, sunflower, train, lizard, sea, kangaroo, elephant, dolphin, leopard, bowl, telephone, pickup_truck, otter, wolf, motorcycle, snake, shark, skunk, shrew, flatfish, cockroach, mushroom, plate, orange, wardrobe, seal
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Base model
microsoft/resnet-101