--- 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_0281) 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 | 0.0003 | | LR Scheduler | constant_with_warmup | | Epochs | 2 | | Max Train Steps | 666 | | Batch Size | 64 | | Weight Decay | 0.05 | | Seed | 281 | | Random Crop | False | | Random Flip | True | ## Performance | Metric | Value | |---|---| | Train Accuracy | 0.2513 | | Val Accuracy | 0.2344 | | Test Accuracy | 0.2452 | ## Training Categories The model was fine-tuned on the following 50 CIFAR100 classes: `wolf`, `rose`, `whale`, `mushroom`, `lamp`, `lizard`, `poppy`, `mountain`, `possum`, `butterfly`, `tank`, `beetle`, `otter`, `seal`, `caterpillar`, `shark`, `bee`, `pine_tree`, `bottle`, `aquarium_fish`, `keyboard`, `bowl`, `oak_tree`, `clock`, `raccoon`, `plain`, `telephone`, `cloud`, `television`, `pickup_truck`, `table`, `streetcar`, `couch`, `willow_tree`, `tulip`, `bicycle`, `spider`, `flatfish`, `bed`, `porcupine`, `bridge`, `lawn_mower`, `baby`, `turtle`, `fox`, `pear`, `snail`, `dinosaur`, `cockroach`, `rocket`