metadata
base_model: microsoft/resnet-101
library_name: transformers
pipeline_tag: image-classification
tags:
- probex
- model-j
- weight-space-learning
Model-J: ResNet Model (model_idx_0002)
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_with_warmup |
| Epochs | 8 |
| Max Train Steps | 2664 |
| Batch Size | 64 |
| Weight Decay | 0.005 |
| Seed | 2 |
| Random Crop | False |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9584 |
| Val Accuracy | 0.8771 |
| Test Accuracy | 0.8828 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
snail, maple_tree, cloud, elephant, trout, sweet_pepper, rocket, mushroom, turtle, streetcar, oak_tree, couch, pine_tree, crab, boy, bed, orchid, apple, tiger, crocodile, caterpillar, chimpanzee, possum, hamster, poppy, pear, lobster, leopard, plain, train, tractor, rabbit, forest, road, porcupine, plate, man, dinosaur, kangaroo, spider, motorcycle, beaver, worm, lion, palm_tree, beetle, aquarium_fish, orange, seal, lizard