--- 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_0057) 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 | cosine | | Epochs | 4 | | Max Train Steps | 1332 | | Batch Size | 64 | | Weight Decay | 0.05 | | Seed | 57 | | Random Crop | True | | Random Flip | True | ## Performance | Metric | Value | |---|---| | Train Accuracy | 0.9948 | | Val Accuracy | 0.9269 | | Test Accuracy | 0.9248 | ## Training Categories The model was fine-tuned on the following 50 CIFAR100 classes: `poppy`, `palm_tree`, `wolf`, `rabbit`, `man`, `orange`, `snail`, `camel`, `otter`, `pickup_truck`, `rocket`, `cattle`, `rose`, `porcupine`, `lion`, `mouse`, `baby`, `pear`, `trout`, `orchid`, `leopard`, `shrew`, `bottle`, `cup`, `wardrobe`, `clock`, `bee`, `crocodile`, `bear`, `aquarium_fish`, `keyboard`, `table`, `sea`, `worm`, `plain`, `bus`, `oak_tree`, `bicycle`, `turtle`, `castle`, `telephone`, `snake`, `pine_tree`, `dinosaur`, `willow_tree`, `tractor`, `couch`, `girl`, `kangaroo`, `train`