| 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_0336) | |
| 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"> | |
| π <a href="https://horwitz.ai/probex" target="_blank">Project</a> | π <a href="https://arxiv.org/abs/2410.13569" target="_blank">Paper</a> | π» <a href="https://github.com/eliahuhorwitz/ProbeX" target="_blank">GitHub</a> | π€ <a href="https://huggingface.co/ProbeX" target="_blank">Dataset</a> | |
| </p> | |
|  | |
| ## Model Details | |
| | Attribute | Value | | |
| |---|---| | |
| | **Subset** | DINO | | |
| | **Split** | test | | |
| | **Base Model** | `facebook/dino-vitb16` | | |
| | **Dataset** | CIFAR100 (50 classes) | | |
| ## Training Hyperparameters | |
| | Parameter | Value | | |
| |---|---| | |
| | Learning Rate | 0.0001 | | |
| | LR Scheduler | constant | | |
| | Epochs | 9 | | |
| | Max Train Steps | 2997 | | |
| | Batch Size | 64 | | |
| | Weight Decay | 0.05 | | |
| | Seed | 336 | | |
| | Random Crop | True | | |
| | Random Flip | False | | |
| ## Performance | |
| | Metric | Value | | |
| |---|---| | |
| | Train Accuracy | 0.9363 | | |
| | Val Accuracy | 0.8336 | | |
| | Test Accuracy | 0.8382 | | |
| ## Training Categories | |
| The model was fine-tuned on the following 50 CIFAR100 classes: | |
| `boy`, `bear`, `butterfly`, `crab`, `woman`, `chair`, `porcupine`, `palm_tree`, `wolf`, `hamster`, `oak_tree`, `raccoon`, `lawn_mower`, `elephant`, `lizard`, `ray`, `man`, `pine_tree`, `house`, `dinosaur`, `cloud`, `tank`, `bicycle`, `worm`, `bowl`, `tractor`, `chimpanzee`, `sunflower`, `leopard`, `whale`, `snail`, `plain`, `train`, `aquarium_fish`, `wardrobe`, `baby`, `lamp`, `streetcar`, `couch`, `shrew`, `snake`, `lion`, `maple_tree`, `poppy`, `telephone`, `camel`, `clock`, `cup`, `cattle`, `caterpillar` | |