| 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_0468) | |
| 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 | 3e-05 | | |
| | LR Scheduler | cosine | | |
| | Epochs | 7 | | |
| | Max Train Steps | 2331 | | |
| | Batch Size | 64 | | |
| | Weight Decay | 0.007 | | |
| | Seed | 468 | | |
| | Random Crop | True | | |
| | Random Flip | True | | |
| ## Performance | |
| | Metric | Value | | |
| |---|---| | |
| | Train Accuracy | 0.7912 | | |
| | Val Accuracy | 0.7749 | | |
| | Test Accuracy | 0.7768 | | |
| ## Training Categories | |
| The model was fine-tuned on the following 50 CIFAR100 classes: | |
| `lawn_mower`, `dolphin`, `kangaroo`, `fox`, `mushroom`, `porcupine`, `can`, `trout`, `crab`, `tractor`, `beetle`, `man`, `leopard`, `crocodile`, `woman`, `skunk`, `boy`, `butterfly`, `cockroach`, `poppy`, `pear`, `maple_tree`, `ray`, `oak_tree`, `skyscraper`, `telephone`, `castle`, `couch`, `lizard`, `train`, `shrew`, `bus`, `forest`, `bed`, `aquarium_fish`, `wolf`, `plain`, `bottle`, `worm`, `orange`, `willow_tree`, `hamster`, `spider`, `raccoon`, `dinosaur`, `bridge`, `mountain`, `house`, `bicycle`, `bee` | |