| 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_0749) | |
| 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** | train | | |
| | **Base Model** | `google/vit-base-patch16-224` | | |
| | **Dataset** | CIFAR100 (50 classes) | | |
| ## Training Hyperparameters | |
| | Parameter | Value | | |
| |---|---| | |
| | Learning Rate | 9e-05 | | |
| | LR Scheduler | constant_with_warmup | | |
| | Epochs | 6 | | |
| | Max Train Steps | 1998 | | |
| | Batch Size | 64 | | |
| | Weight Decay | 0.007 | | |
| | Seed | 749 | | |
| | Random Crop | True | | |
| | Random Flip | True | | |
| ## Performance | |
| | Metric | Value | | |
| |---|---| | |
| | Train Accuracy | 0.9639 | | |
| | Val Accuracy | 0.9373 | | |
| | Test Accuracy | 0.9302 | | |
| ## Training Categories | |
| The model was fine-tuned on the following 50 CIFAR100 classes: | |
| `possum`, `flatfish`, `tractor`, `lawn_mower`, `television`, `house`, `road`, `chair`, `caterpillar`, `wolf`, `oak_tree`, `dinosaur`, `rocket`, `sunflower`, `mountain`, `snail`, `shark`, `lobster`, `motorcycle`, `tiger`, `sweet_pepper`, `mouse`, `chimpanzee`, `seal`, `aquarium_fish`, `pear`, `cup`, `wardrobe`, `dolphin`, `can`, `cloud`, `plain`, `orchid`, `butterfly`, `hamster`, `man`, `baby`, `spider`, `bus`, `crocodile`, `palm_tree`, `bowl`, `train`, `worm`, `sea`, `cattle`, `orange`, `boy`, `skyscraper`, `lion` | |