| base_model: google/vit-base-patch16-224 | |
| library_name: transformers | |
| pipeline_tag: image-classification | |
| tags: | |
| - probex | |
| - model-j | |
| - weight-space-learning | |
| # Model-J: SupViT Model (model_idx_0735) | |
| 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** | SupViT | | |
| | **Split** | test | | |
| | **Base Model** | `google/vit-base-patch16-224` | | |
| | **Dataset** | CIFAR100 (50 classes) | | |
| ## Training Hyperparameters | |
| | Parameter | Value | | |
| |---|---| | |
| | Learning Rate | 0.0001 | | |
| | LR Scheduler | cosine_with_restarts | | |
| | Epochs | 9 | | |
| | Max Train Steps | 2997 | | |
| | Batch Size | 64 | | |
| | Weight Decay | 0.009 | | |
| | Seed | 735 | | |
| | Random Crop | True | | |
| | Random Flip | True | | |
| ## Performance | |
| | Metric | Value | | |
| |---|---| | |
| | Train Accuracy | 0.9993 | | |
| | Val Accuracy | 0.9563 | | |
| | Test Accuracy | 0.9512 | | |
| ## Training Categories | |
| The model was fine-tuned on the following 50 CIFAR100 classes: | |
| `shrew`, `raccoon`, `crocodile`, `house`, `tiger`, `trout`, `apple`, `camel`, `shark`, `bed`, `motorcycle`, `skyscraper`, `cup`, `snake`, `television`, `rabbit`, `sweet_pepper`, `couch`, `flatfish`, `orange`, `porcupine`, `bicycle`, `cloud`, `cattle`, `plain`, `boy`, `elephant`, `palm_tree`, `poppy`, `lamp`, `forest`, `plate`, `possum`, `sunflower`, `turtle`, `mountain`, `dolphin`, `oak_tree`, `skunk`, `caterpillar`, `beaver`, `leopard`, `orchid`, `sea`, `streetcar`, `lion`, `whale`, `rocket`, `snail`, `wolf` | |