| 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_0421) | |
| 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** | test | | |
| | **Base Model** | `facebook/dino-vitb16` | | |
| | **Dataset** | CIFAR100 (50 classes) | | |
| ## Training Hyperparameters | |
| | Parameter | Value | | |
| |---|---| | |
| | Learning Rate | 9e-05 | | |
| | LR Scheduler | linear | | |
| | Epochs | 5 | | |
| | Max Train Steps | 1665 | | |
| | Batch Size | 64 | | |
| | Weight Decay | 0.01 | | |
| | Seed | 421 | | |
| | Random Crop | True | | |
| | Random Flip | True | | |
| ## Performance | |
| | Metric | Value | | |
| |---|---| | |
| | Train Accuracy | 0.9995 | | |
| | Val Accuracy | 0.9240 | | |
| | Test Accuracy | 0.9248 | | |
| ## Training Categories | |
| The model was fine-tuned on the following 50 CIFAR100 classes: | |
| `turtle`, `trout`, `dolphin`, `snail`, `bicycle`, `whale`, `telephone`, `leopard`, `elephant`, `caterpillar`, `kangaroo`, `shrew`, `lawn_mower`, `fox`, `sweet_pepper`, `keyboard`, `motorcycle`, `shark`, `cattle`, `tulip`, `butterfly`, `forest`, `rabbit`, `baby`, `possum`, `cockroach`, `palm_tree`, `chimpanzee`, `skunk`, `train`, `bear`, `bee`, `crocodile`, `worm`, `mouse`, `mushroom`, `beaver`, `raccoon`, `lion`, `pickup_truck`, `pine_tree`, `mountain`, `dinosaur`, `can`, `tiger`, `ray`, `skyscraper`, `apple`, `sunflower`, `lamp` | |