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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_0903)
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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 | 5e-05 |
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| LR Scheduler | constant |
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| Epochs | 3 |
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| Max Train Steps | 999 |
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| Batch Size | 64 |
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| Weight Decay | 0.03 |
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| Seed | 903 |
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| Random Crop | False |
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| Random Flip | False |
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## Performance
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| Metric | Value |
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|---|---|
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| Train Accuracy | 0.8884 |
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| Val Accuracy | 0.8360 |
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| Test Accuracy | 0.8326 |
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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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`rabbit`, `bowl`, `cloud`, `motorcycle`, `tulip`, `porcupine`, `pine_tree`, `shrew`, `table`, `rose`, `dinosaur`, `baby`, `fox`, `hamster`, `crocodile`, `sweet_pepper`, `raccoon`, `man`, `plate`, `caterpillar`, `telephone`, `leopard`, `beaver`, `bottle`, `streetcar`, `couch`, `crab`, `worm`, `can`, `castle`, `road`, `tractor`, `chair`, `wardrobe`, `bear`, `aquarium_fish`, `house`, `skunk`, `trout`, `forest`, `rocket`, `cup`, `flatfish`, `pear`, `bed`, `cockroach`, `tank`, `shark`, `girl`, `lobster`
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