| 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_0164) | |
| 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 | |
| <p align="center"> | |
| 🌐 <a href="https://horwitz.ai/probex" target="_blank">Project</a> | 📃 <a href="https://arxiv.org/abs/2410.13569" target="_blank">Paper</a> | 💻 <a href="https://github.com/eliahuhorwitz/ProbeX" target="_blank">GitHub</a> | 🤗 <a href="https://huggingface.co/ProbeX" target="_blank">Dataset</a> | |
| </p> | |
|  | |
| ## Model Details | |
| | Attribute | Value | | |
| |---|---| | |
| | **Subset** | ResNet | | |
| | **Split** | train | | |
| | **Base Model** | `microsoft/resnet-101` | | |
| | **Dataset** | CIFAR100 (50 classes) | | |
| ## Training Hyperparameters | |
| | Parameter | Value | | |
| |---|---| | |
| | Learning Rate | 3e-05 | | |
| | LR Scheduler | cosine_with_restarts | | |
| | Epochs | 9 | | |
| | Max Train Steps | 2997 | | |
| | Batch Size | 64 | | |
| | Weight Decay | 0.03 | | |
| | Seed | 164 | | |
| | Random Crop | True | | |
| | Random Flip | True | | |
| ## Performance | |
| | Metric | Value | | |
| |---|---| | |
| | Train Accuracy | 0.8189 | | |
| | Val Accuracy | 0.7899 | | |
| | Test Accuracy | 0.7900 | | |
| ## Training Categories | |
| The model was fine-tuned on the following 50 CIFAR100 classes: | |
| `shrew`, `bowl`, `chimpanzee`, `cattle`, `camel`, `orchid`, `mountain`, `mushroom`, `forest`, `chair`, `dolphin`, `wardrobe`, `skyscraper`, `pine_tree`, `clock`, `aquarium_fish`, `apple`, `wolf`, `tank`, `couch`, `fox`, `skunk`, `castle`, `ray`, `oak_tree`, `pickup_truck`, `tulip`, `palm_tree`, `raccoon`, `man`, `willow_tree`, `crocodile`, `seal`, `beaver`, `kangaroo`, `flatfish`, `maple_tree`, `orange`, `lamp`, `lizard`, `telephone`, `turtle`, `can`, `bear`, `bicycle`, `crab`, `caterpillar`, `motorcycle`, `bee`, `snake` | |