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 | 0.0005 |
| LR Scheduler | constant_with_warmup |
| Epochs | 3 |
| Max Train Steps | 999 |
| Batch Size | 64 |
| Weight Decay | 0.005 |
| Seed | 114 |
| Random Crop | False |
| Random Flip | True |
| Metric | Value |
|---|---|
| Train Accuracy | 0.9578 |
| Val Accuracy | 0.8789 |
| Test Accuracy | 0.8780 |
The model was fine-tuned on the following 50 CIFAR100 classes:
tractor, woman, lion, hamster, castle, rocket, fox, possum, kangaroo, shrew, wardrobe, wolf, lobster, bicycle, crocodile, pine_tree, elephant, bottle, seal, lizard, lamp, man, skunk, lawn_mower, motorcycle, plain, snake, cattle, orange, porcupine, ray, keyboard, crab, television, mountain, worm, maple_tree, turtle, boy, clock, sweet_pepper, bear, whale, shark, tank, cloud, mouse, poppy, camel, leopard
Base model
microsoft/resnet-101