| 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_0040) | |
| 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** | train | | |
| | **Base Model** | `facebook/dino-vitb16` | | |
| | **Dataset** | CIFAR100 (50 classes) | | |
| ## Training Hyperparameters | |
| | Parameter | Value | | |
| |---|---| | |
| | Learning Rate | 3e-05 | | |
| | LR Scheduler | cosine | | |
| | Epochs | 6 | | |
| | Max Train Steps | 1998 | | |
| | Batch Size | 64 | | |
| | Weight Decay | 0.007 | | |
| | Seed | 40 | | |
| | Random Crop | True | | |
| | Random Flip | True | | |
| ## Performance | |
| | Metric | Value | | |
| |---|---| | |
| | Train Accuracy | 0.9945 | | |
| | Val Accuracy | 0.9163 | | |
| | Test Accuracy | 0.9174 | | |
| ## Training Categories | |
| The model was fine-tuned on the following 50 CIFAR100 classes: | |
| `baby`, `beaver`, `boy`, `pine_tree`, `bus`, `sunflower`, `lizard`, `otter`, `maple_tree`, `cup`, `bed`, `chair`, `caterpillar`, `cloud`, `skunk`, `orange`, `couch`, `squirrel`, `ray`, `kangaroo`, `rocket`, `girl`, `oak_tree`, `plain`, `bear`, `cattle`, `snake`, `raccoon`, `chimpanzee`, `fox`, `rose`, `television`, `cockroach`, `road`, `possum`, `wardrobe`, `poppy`, `palm_tree`, `dolphin`, `bridge`, `dinosaur`, `elephant`, `whale`, `sea`, `shrew`, `apple`, `keyboard`, `mountain`, `worm`, `beetle` | |