| base_model: microsoft/resnet-101 | |
| library_name: transformers | |
| pipeline_tag: image-classification | |
| tags: | |
| - probex | |
| - model-j | |
| - weight-space-learning | |
| # Model-J: ResNet Model (model_idx_0211) | |
| 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 | |
| <p align="center"> | |
| 🌐 <a href="https://horwitz.ai/probex" target="_blank">Project</a> | 📃 <a href="https://arxiv.org/abs/2410.13569" target="_blank">Paper</a> | 💻 <a href="https://github.com/eliahuhorwitz/ProbeX" target="_blank">GitHub</a> | 🤗 <a href="https://huggingface.co/ProbeX" target="_blank">Dataset</a> | |
| </p> | |
|  | |
| ## Model Details | |
| | Attribute | Value | | |
| |---|---| | |
| | **Subset** | ResNet | | |
| | **Split** | train | | |
| | **Base Model** | `microsoft/resnet-101` | | |
| | **Dataset** | CIFAR100 (50 classes) | | |
| ## Training Hyperparameters | |
| | Parameter | Value | | |
| |---|---| | |
| | Learning Rate | 9e-05 | | |
| | LR Scheduler | cosine_with_restarts | | |
| | Epochs | 8 | | |
| | Max Train Steps | 2664 | | |
| | Batch Size | 64 | | |
| | Weight Decay | 0.05 | | |
| | Seed | 211 | | |
| | Random Crop | True | | |
| | Random Flip | True | | |
| ## Performance | |
| | Metric | Value | | |
| |---|---| | |
| | Train Accuracy | 0.9351 | | |
| | Val Accuracy | 0.8709 | | |
| | Test Accuracy | 0.8710 | | |
| ## Training Categories | |
| The model was fine-tuned on the following 50 CIFAR100 classes: | |
| `sea`, `tank`, `camel`, `squirrel`, `mouse`, `spider`, `bee`, `butterfly`, `poppy`, `wardrobe`, `bear`, `bottle`, `pear`, `beaver`, `otter`, `telephone`, `bus`, `aquarium_fish`, `tractor`, `crab`, `couch`, `porcupine`, `plain`, `elephant`, `skyscraper`, `castle`, `raccoon`, `bicycle`, `worm`, `maple_tree`, `rose`, `chimpanzee`, `flatfish`, `snake`, `keyboard`, `willow_tree`, `mountain`, `skunk`, `orange`, `bed`, `road`, `shrew`, `lizard`, `streetcar`, `beetle`, `woman`, `girl`, `tulip`, `television`, `cattle` | |