--- 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_0331) 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 | 5e-05 | | LR Scheduler | constant_with_warmup | | Epochs | 8 | | Max Train Steps | 2664 | | Batch Size | 64 | | Weight Decay | 0.007 | | Seed | 331 | | Random Crop | False | | Random Flip | True | ## Performance | Metric | Value | |---|---| | Train Accuracy | 0.9780 | | Val Accuracy | 0.9085 | | Test Accuracy | 0.8992 | ## Training Categories The model was fine-tuned on the following 50 CIFAR100 classes: `boy`, `wolf`, `couch`, `squirrel`, `clock`, `bear`, `beetle`, `rocket`, `road`, `camel`, `fox`, `elephant`, `cattle`, `raccoon`, `crab`, `train`, `flatfish`, `apple`, `skunk`, `palm_tree`, `rose`, `forest`, `pine_tree`, `tractor`, `tank`, `bridge`, `wardrobe`, `television`, `skyscraper`, `aquarium_fish`, `telephone`, `streetcar`, `dinosaur`, `man`, `orchid`, `rabbit`, `kangaroo`, `crocodile`, `leopard`, `pickup_truck`, `lizard`, `mouse`, `bottle`, `sunflower`, `snake`, `lion`, `shark`, `seal`, `mushroom`, `mountain`