Model-J ResNet
Collection
1001 items
โข
Updated
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
๐ Project | ๐ Paper | ๐ป GitHub | ๐ค Dataset
| Attribute | Value |
|---|---|
| Subset | ResNet |
| Split | train |
| Base Model | microsoft/resnet-101 |
| Dataset | CIFAR100 (50 classes) |
| Parameter | Value |
|---|---|
| Learning Rate | 9e-05 |
| LR Scheduler | linear |
| Epochs | 6 |
| Max Train Steps | 1998 |
| Batch Size | 64 |
| Weight Decay | 0.009 |
| Seed | 119 |
| Random Crop | True |
| Random Flip | True |
| Metric | Value |
|---|---|
| Train Accuracy | 0.8900 |
| Val Accuracy | 0.8333 |
| Test Accuracy | 0.8350 |
The model was fine-tuned on the following 50 CIFAR100 classes:
bear, wardrobe, snail, bowl, seal, plain, butterfly, rocket, oak_tree, mouse, tank, trout, tulip, maple_tree, crab, dinosaur, palm_tree, shark, chair, turtle, lion, willow_tree, worm, motorcycle, poppy, tractor, porcupine, forest, cloud, lawn_mower, ray, sunflower, streetcar, elephant, whale, bus, camel, orchid, pine_tree, man, telephone, bottle, fox, spider, leopard, couch, train, rabbit, boy, otter
Base model
microsoft/resnet-101