| 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_0353) | |
| 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 | 0.0001 | | |
| | LR Scheduler | constant | | |
| | Epochs | 5 | | |
| | Max Train Steps | 1665 | | |
| | Batch Size | 64 | | |
| | Weight Decay | 0.01 | | |
| | Seed | 353 | | |
| | Random Crop | True | | |
| | Random Flip | False | | |
| ## Performance | |
| | Metric | Value | | |
| |---|---| | |
| | Train Accuracy | 0.9514 | | |
| | Val Accuracy | 0.8773 | | |
| | Test Accuracy | 0.8652 | | |
| ## Training Categories | |
| The model was fine-tuned on the following 50 CIFAR100 classes: | |
| `shrew`, `ray`, `maple_tree`, `tractor`, `worm`, `cup`, `road`, `willow_tree`, `hamster`, `snail`, `snake`, `leopard`, `plate`, `lobster`, `pickup_truck`, `streetcar`, `chair`, `beetle`, `baby`, `keyboard`, `sunflower`, `raccoon`, `crab`, `skyscraper`, `sweet_pepper`, `table`, `seal`, `woman`, `mouse`, `trout`, `crocodile`, `shark`, `pear`, `bicycle`, `camel`, `cloud`, `can`, `whale`, `motorcycle`, `boy`, `man`, `pine_tree`, `lizard`, `kangaroo`, `lawn_mower`, `tiger`, `rabbit`, `fox`, `rose`, `house` | |