--- 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_0052) 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** | val | | **Base Model** | `facebook/dino-vitb16` | | **Dataset** | CIFAR100 (50 classes) | ## Training Hyperparameters | Parameter | Value | |---|---| | Learning Rate | 3e-05 | | LR Scheduler | cosine_with_restarts | | Epochs | 8 | | Max Train Steps | 2664 | | Batch Size | 64 | | Weight Decay | 0.03 | | Seed | 52 | | Random Crop | False | | Random Flip | True | ## Performance | Metric | Value | |---|---| | Train Accuracy | 0.9999 | | Val Accuracy | 0.9272 | | Test Accuracy | 0.9364 | ## Training Categories The model was fine-tuned on the following 50 CIFAR100 classes: `fox`, `worm`, `trout`, `streetcar`, `couch`, `elephant`, `mountain`, `sunflower`, `possum`, `lawn_mower`, `telephone`, `tiger`, `kangaroo`, `tank`, `beetle`, `aquarium_fish`, `tractor`, `boy`, `tulip`, `plate`, `chimpanzee`, `otter`, `mushroom`, `poppy`, `cloud`, `television`, `dinosaur`, `whale`, `rose`, `pickup_truck`, `wolf`, `willow_tree`, `rocket`, `sweet_pepper`, `bowl`, `orchid`, `skunk`, `chair`, `snake`, `sea`, `castle`, `plain`, `caterpillar`, `crocodile`, `bus`, `train`, `wardrobe`, `baby`, `can`, `raccoon`