| 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_0283) | |
| 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 | 0.0003 | | |
| | LR Scheduler | linear | | |
| | Epochs | 9 | | |
| | Max Train Steps | 2997 | | |
| | Batch Size | 64 | | |
| | Weight Decay | 0.01 | | |
| | Seed | 283 | | |
| | Random Crop | False | | |
| | Random Flip | True | | |
| ## Performance | |
| | Metric | Value | | |
| |---|---| | |
| | Train Accuracy | 0.9980 | | |
| | Val Accuracy | 0.8936 | | |
| | Test Accuracy | 0.8888 | | |
| ## Training Categories | |
| The model was fine-tuned on the following 50 CIFAR100 classes: | |
| `chair`, `worm`, `maple_tree`, `boy`, `orchid`, `oak_tree`, `lion`, `rabbit`, `road`, `whale`, `crab`, `can`, `willow_tree`, `man`, `possum`, `crocodile`, `telephone`, `table`, `bear`, `lawn_mower`, `caterpillar`, `otter`, `wolf`, `beetle`, `orange`, `bus`, `shrew`, `palm_tree`, `lobster`, `elephant`, `kangaroo`, `bicycle`, `shark`, `tiger`, `bridge`, `television`, `snake`, `flatfish`, `chimpanzee`, `poppy`, `skunk`, `forest`, `mushroom`, `streetcar`, `pear`, `bowl`, `dolphin`, `seal`, `porcupine`, `sea` | |