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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_0388)
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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_with_warmup |
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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 | 0.05 |
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| Seed | 388 |
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| Random Crop | True |
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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 | 0.8327 |
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| Val Accuracy | 0.8128 |
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| Test Accuracy | 0.8150 |
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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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`cup`, `man`, `tiger`, `woman`, `aquarium_fish`, `otter`, `television`, `bear`, `wolf`, `lawn_mower`, `squirrel`, `bottle`, `flatfish`, `keyboard`, `leopard`, `sea`, `shark`, `lion`, `rose`, `camel`, `trout`, `pear`, `willow_tree`, `motorcycle`, `sweet_pepper`, `house`, `pine_tree`, `chimpanzee`, `table`, `bee`, `crocodile`, `kangaroo`, `streetcar`, `fox`, `tractor`, `tulip`, `bicycle`, `spider`, `mouse`, `dolphin`, `turtle`, `couch`, `skunk`, `apple`, `mountain`, `beetle`, `skyscraper`, `lamp`, `crab`, `oak_tree`
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