--- 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_0464) 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 | constant | | Epochs | 7 | | Max Train Steps | 2331 | | Batch Size | 64 | | Weight Decay | 0.01 | | Seed | 464 | | Random Crop | False | | Random Flip | True | ## Performance | Metric | Value | |---|---| | Train Accuracy | 0.9889 | | Val Accuracy | 0.9157 | | Test Accuracy | 0.9132 | ## Training Categories The model was fine-tuned on the following 50 CIFAR100 classes: `keyboard`, `lobster`, `bus`, `clock`, `porcupine`, `train`, `chimpanzee`, `man`, `maple_tree`, `bed`, `spider`, `streetcar`, `skunk`, `rose`, `fox`, `kangaroo`, `ray`, `house`, `bridge`, `bottle`, `leopard`, `bowl`, `apple`, `rocket`, `otter`, `plain`, `crocodile`, `couch`, `snake`, `mushroom`, `lamp`, `forest`, `castle`, `bee`, `shrew`, `aquarium_fish`, `sweet_pepper`, `lizard`, `squirrel`, `pickup_truck`, `snail`, `pear`, `motorcycle`, `whale`, `poppy`, `cattle`, `crab`, `cockroach`, `butterfly`, `cup`