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README.md
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---
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base_model: microsoft/resnet-101
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library_name: transformers
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pipeline_tag: image-classification
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tags:
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- probex
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- model-j
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- weight-space-learning
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---
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# Model-J: ResNet Model (model_idx_0498)
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This model is part of the **Model-J** dataset, introduced in:
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**Learning on Model Weights using Tree Experts** (CVPR 2025) by Eliahu Horwitz*, Bar Cavia*, Jonathan Kahana*, Yedid Hoshen
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<p align="center">
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🌐 <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>
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</p>
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## Model Details
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| Attribute | Value |
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|---|---|
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| **Subset** | ResNet |
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| **Split** | train |
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| **Base Model** | `microsoft/resnet-101` |
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| **Dataset** | CIFAR100 (50 classes) |
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## Training Hyperparameters
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| Parameter | Value |
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|---|---|
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| Learning Rate | 7e-05 |
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| LR Scheduler | constant |
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| Epochs | 7 |
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| Max Train Steps | 2331 |
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| Batch Size | 64 |
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| Weight Decay | 0.005 |
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| Seed | 498 |
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| Random Crop | True |
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| Random Flip | True |
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## Performance
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| Metric | Value |
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|---|---|
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| Train Accuracy | 0.9511 |
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| Val Accuracy | 0.8832 |
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| Test Accuracy | 0.8788 |
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## Training Categories
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The model was fine-tuned on the following 50 CIFAR100 classes:
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`snail`, `crocodile`, `apple`, `worm`, `snake`, `pickup_truck`, `sea`, `otter`, `forest`, `chair`, `pear`, `spider`, `palm_tree`, `shrew`, `cloud`, `skyscraper`, `lizard`, `train`, `tank`, `fox`, `willow_tree`, `sunflower`, `turtle`, `plain`, `telephone`, `bottle`, `orange`, `wolf`, `flatfish`, `leopard`, `mountain`, `chimpanzee`, `elephant`, `cup`, `streetcar`, `oak_tree`, `pine_tree`, `house`, `clock`, `mushroom`, `can`, `beaver`, `lamp`, `lion`, `keyboard`, `butterfly`, `possum`, `boy`, `sweet_pepper`, `bus`
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