| 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_0247) | |
| 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 | 2 | | |
| | Max Train Steps | 666 | | |
| | Batch Size | 64 | | |
| | Weight Decay | 0.05 | | |
| | Seed | 247 | | |
| | Random Crop | True | | |
| | Random Flip | True | | |
| ## Performance | |
| | Metric | Value | | |
| |---|---| | |
| | Train Accuracy | 0.7157 | | |
| | Val Accuracy | 0.6963 | | |
| | Test Accuracy | 0.6972 | | |
| ## Training Categories | |
| The model was fine-tuned on the following 50 CIFAR100 classes: | |
| `flatfish`, `caterpillar`, `beaver`, `dolphin`, `plain`, `sweet_pepper`, `apple`, `wolf`, `chimpanzee`, `streetcar`, `hamster`, `mushroom`, `clock`, `cattle`, `aquarium_fish`, `woman`, `trout`, `mountain`, `shark`, `tank`, `cloud`, `bear`, `dinosaur`, `keyboard`, `ray`, `willow_tree`, `telephone`, `plate`, `bus`, `fox`, `beetle`, `girl`, `tulip`, `porcupine`, `snail`, `orange`, `sunflower`, `skyscraper`, `possum`, `castle`, `lamp`, `wardrobe`, `shrew`, `forest`, `sea`, `chair`, `house`, `bicycle`, `can`, `kangaroo` | |