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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_0924)
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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 | 9e-05 |
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| LR Scheduler | cosine |
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| Epochs | 8 |
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| Max Train Steps | 2664 |
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| Batch Size | 64 |
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| Weight Decay | 0.05 |
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| Seed | 924 |
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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 | 0.9294 |
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| Val Accuracy | 0.8741 |
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| Test Accuracy | 0.8682 |
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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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`plain`, `bridge`, `caterpillar`, `forest`, `camel`, `worm`, `train`, `television`, `rose`, `crocodile`, `raccoon`, `tank`, `bear`, `oak_tree`, `leopard`, `shrew`, `otter`, `cattle`, `plate`, `bottle`, `shark`, `chimpanzee`, `sea`, `orchid`, `bee`, `chair`, `lobster`, `dolphin`, `tractor`, `pickup_truck`, `lizard`, `pear`, `snail`, `skunk`, `maple_tree`, `willow_tree`, `beetle`, `flatfish`, `can`, `trout`, `porcupine`, `couch`, `woman`, `motorcycle`, `telephone`, `whale`, `ray`, `cockroach`, `apple`, `keyboard`
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