| base_model: facebook/dino-vitb16 | |
| library_name: transformers | |
| pipeline_tag: image-classification | |
| tags: | |
| - probex | |
| - model-j | |
| - weight-space-learning | |
| # Model-J: DINO Model (model_idx_0356) | |
| 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** | DINO | | |
| | **Split** | train | | |
| | **Base Model** | `facebook/dino-vitb16` | | |
| | **Dataset** | CIFAR100 (50 classes) | | |
| ## Training Hyperparameters | |
| | Parameter | Value | | |
| |---|---| | |
| | Learning Rate | 0.0003 | | |
| | LR Scheduler | constant_with_warmup | | |
| | Epochs | 7 | | |
| | Max Train Steps | 2331 | | |
| | Batch Size | 64 | | |
| | Weight Decay | 0.01 | | |
| | Seed | 356 | | |
| | Random Crop | True | | |
| | Random Flip | False | | |
| ## Performance | |
| | Metric | Value | | |
| |---|---| | |
| | Train Accuracy | 0.4686 | | |
| | Val Accuracy | 0.3755 | | |
| | Test Accuracy | 0.4018 | | |
| ## Training Categories | |
| The model was fine-tuned on the following 50 CIFAR100 classes: | |
| `rose`, `possum`, `apple`, `seal`, `pear`, `camel`, `telephone`, `pickup_truck`, `palm_tree`, `bus`, `willow_tree`, `dolphin`, `motorcycle`, `porcupine`, `plain`, `rabbit`, `lamp`, `wardrobe`, `oak_tree`, `fox`, `tank`, `tractor`, `ray`, `can`, `table`, `chimpanzee`, `rocket`, `woman`, `orange`, `pine_tree`, `otter`, `mouse`, `elephant`, `hamster`, `cattle`, `aquarium_fish`, `baby`, `maple_tree`, `bear`, `shark`, `tiger`, `raccoon`, `bicycle`, `beaver`, `cup`, `trout`, `caterpillar`, `house`, `skunk`, `squirrel` | |