--- 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_0030) 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

🌐 Project | 📃 Paper | 💻 GitHub | 🤗 Dataset

![ProbeX](https://raw.githubusercontent.com/eliahuhorwitz/ProbeX/main/imgs/poster.png) ## Model Details | Attribute | Value | |---|---| | **Subset** | DINO | | **Split** | train | | **Base Model** | `facebook/dino-vitb16` | | **Dataset** | CIFAR100 (50 classes) | ## Training Hyperparameters | Parameter | Value | |---|---| | Learning Rate | 9e-05 | | LR Scheduler | cosine | | Epochs | 7 | | Max Train Steps | 2331 | | Batch Size | 64 | | Weight Decay | 0.007 | | Seed | 30 | | Random Crop | True | | Random Flip | False | ## Performance | Metric | Value | |---|---| | Train Accuracy | 0.9992 | | Val Accuracy | 0.9195 | | Test Accuracy | 0.9214 | ## Training Categories The model was fine-tuned on the following 50 CIFAR100 classes: `skyscraper`, `rabbit`, `orchid`, `porcupine`, `hamster`, `mouse`, `train`, `chimpanzee`, `mushroom`, `snail`, `bus`, `plain`, `beetle`, `apple`, `caterpillar`, `telephone`, `wolf`, `raccoon`, `cloud`, `tractor`, `bear`, `kangaroo`, `bicycle`, `can`, `road`, `dolphin`, `worm`, `beaver`, `bridge`, `turtle`, `otter`, `orange`, `rose`, `tulip`, `palm_tree`, `clock`, `fox`, `maple_tree`, `ray`, `table`, `lobster`, `bowl`, `streetcar`, `cup`, `chair`, `willow_tree`, `pear`, `elephant`, `woman`, `rocket`