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 | 0.0003 |
| LR Scheduler | cosine_with_restarts |
| Epochs | 8 |
| Max Train Steps | 2664 |
| Batch Size | 64 |
| Weight Decay | 0.01 |
| Seed | 544 |
| Random Crop | False |
| Random Flip | False |
| Metric | Value |
|---|---|
| Train Accuracy | 0.9990 |
| Val Accuracy | 0.9091 |
| Test Accuracy | 0.9020 |
The model was fine-tuned on the following 50 CIFAR100 classes:
beaver, mouse, flatfish, woman, otter, orange, kangaroo, lamp, lawn_mower, possum, mountain, plain, bed, girl, snail, rabbit, skyscraper, worm, streetcar, cattle, keyboard, lobster, train, butterfly, dolphin, tractor, poppy, bicycle, lion, chair, pear, motorcycle, aquarium_fish, apple, cloud, wardrobe, house, porcupine, oak_tree, pine_tree, rose, bus, spider, dinosaur, whale, maple_tree, crocodile, can, sweet_pepper, raccoon
Base model
microsoft/resnet-101