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_0902)
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 | test |
| Base Model | microsoft/resnet-101 |
| Dataset | CIFAR100 (50 classes) |
Training Hyperparameters
| Parameter | Value |
|---|---|
| Learning Rate | 3e-05 |
| LR Scheduler | constant_with_warmup |
| Epochs | 9 |
| Max Train Steps | 2997 |
| Batch Size | 64 |
| Weight Decay | 0.01 |
| Seed | 902 |
| Random Crop | False |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9187 |
| Val Accuracy | 0.8613 |
| Test Accuracy | 0.8508 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
can, shrew, bowl, fox, wardrobe, bear, turtle, pear, porcupine, whale, beetle, possum, wolf, ray, lion, bottle, train, kangaroo, maple_tree, flatfish, plain, crocodile, forest, rabbit, girl, mouse, worm, streetcar, clock, camel, tiger, leopard, table, cockroach, willow_tree, bus, shark, spider, woman, trout, cattle, orchid, man, rose, skyscraper, caterpillar, pine_tree, lamp, bicycle, tractor