| 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_0837) | |
| 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 | 0.0003 | | |
| | LR Scheduler | cosine | | |
| | Epochs | 2 | | |
| | Max Train Steps | 666 | | |
| | Batch Size | 64 | | |
| | Weight Decay | 0.007 | | |
| | Seed | 837 | | |
| | Random Crop | False | | |
| | Random Flip | True | | |
| ## Performance | |
| | Metric | Value | | |
| |---|---| | |
| | Train Accuracy | 0.9385 | | |
| | Val Accuracy | 0.8997 | | |
| | Test Accuracy | 0.8930 | | |
| ## Training Categories | |
| The model was fine-tuned on the following 50 CIFAR100 classes: | |
| `beetle`, `orange`, `bowl`, `tulip`, `sweet_pepper`, `table`, `lamp`, `mountain`, `pickup_truck`, `lion`, `squirrel`, `cockroach`, `kangaroo`, `rose`, `porcupine`, `hamster`, `turtle`, `lobster`, `cattle`, `caterpillar`, `train`, `palm_tree`, `fox`, `wolf`, `wardrobe`, `mouse`, `plain`, `bottle`, `motorcycle`, `beaver`, `shark`, `lizard`, `lawn_mower`, `pine_tree`, `tank`, `trout`, `snake`, `tractor`, `sunflower`, `snail`, `couch`, `crab`, `seal`, `castle`, `chair`, `elephant`, `raccoon`, `girl`, `bee`, `can` | |