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_0267)
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 | val |
| Base Model | microsoft/resnet-101 |
| Dataset | CIFAR100 (50 classes) |
Training Hyperparameters
| Parameter | Value |
|---|---|
| Learning Rate | 0.0005 |
| LR Scheduler | linear |
| Epochs | 9 |
| Max Train Steps | 2997 |
| Batch Size | 64 |
| Weight Decay | 0.009 |
| Seed | 267 |
| Random Crop | True |
| Random Flip | False |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9987 |
| Val Accuracy | 0.8960 |
| Test Accuracy | 0.9032 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
can, plate, chimpanzee, sea, camel, road, clock, raccoon, beetle, television, seal, lobster, woman, caterpillar, orange, tulip, chair, train, oak_tree, telephone, willow_tree, dinosaur, squirrel, sweet_pepper, mountain, lawn_mower, whale, shrew, worm, bear, lamp, tank, man, fox, table, rose, dolphin, bowl, motorcycle, rabbit, streetcar, ray, skyscraper, house, pickup_truck, pear, shark, spider, bridge, tractor