| 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_0275) | |
| 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 | 0.0003 | | |
| | LR Scheduler | constant_with_warmup | | |
| | Epochs | 8 | | |
| | Max Train Steps | 2664 | | |
| | Batch Size | 64 | | |
| | Weight Decay | 0.005 | | |
| | Seed | 275 | | |
| | Random Crop | True | | |
| | Random Flip | False | | |
| ## Performance | |
| | Metric | Value | | |
| |---|---| | |
| | Train Accuracy | 0.9116 | | |
| | Val Accuracy | 0.8560 | | |
| | Test Accuracy | 0.8558 | | |
| ## Training Categories | |
| The model was fine-tuned on the following 50 CIFAR100 classes: | |
| `snail`, `crocodile`, `hamster`, `raccoon`, `maple_tree`, `possum`, `fox`, `spider`, `oak_tree`, `boy`, `sunflower`, `cloud`, `mouse`, `trout`, `castle`, `sweet_pepper`, `crab`, `beaver`, `turtle`, `tank`, `poppy`, `lizard`, `girl`, `kangaroo`, `aquarium_fish`, `forest`, `ray`, `rose`, `clock`, `rocket`, `lawn_mower`, `flatfish`, `whale`, `camel`, `chair`, `skunk`, `cattle`, `man`, `dolphin`, `wolf`, `bottle`, `plate`, `motorcycle`, `porcupine`, `telephone`, `mountain`, `baby`, `train`, `sea`, `butterfly` | |