--- base_model: google/vit-base-patch16-224 library_name: transformers pipeline_tag: image-classification tags: - probex - model-j - weight-space-learning --- # Model-J: SupViT 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
 ## Model Details | Attribute | Value | |---|---| | **Subset** | SupViT | | **Split** | val | | **Base Model** | `google/vit-base-patch16-224` | | **Dataset** | CIFAR100 (50 classes) | ## Training Hyperparameters | Parameter | Value | |---|---| | Learning Rate | 0.0005 | | LR Scheduler | constant | | Epochs | 4 | | Max Train Steps | 1332 | | Batch Size | 64 | | Weight Decay | 0.03 | | Seed | 52 | | Random Crop | False | | Random Flip | True | ## Performance | Metric | Value | |---|---| | Train Accuracy | 0.9386 | | Val Accuracy | 0.8181 | | Test Accuracy | 0.8134 | ## Training Categories The model was fine-tuned on the following 50 CIFAR100 classes: `rabbit`, `woman`, `plain`, `butterfly`, `dinosaur`, `cloud`, `couch`, `hamster`, `kangaroo`, `bicycle`, `bus`, `baby`, `lion`, `crocodile`, `television`, `plate`, `bottle`, `palm_tree`, `cockroach`, `tank`, `snail`, `seal`, `raccoon`, `sweet_pepper`, `squirrel`, `caterpillar`, `snake`, `possum`, `pine_tree`, `pear`, `otter`, `wolf`, `house`, `can`, `rose`, `lobster`, `clock`, `girl`, `elephant`, `keyboard`, `mouse`, `turtle`, `bowl`, `flatfish`, `worm`, `apple`, `shark`, `skunk`, `telephone`, `wardrobe`