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README.md
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---
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base_model: google/vit-base-patch16-224
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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: SupViT Model (model_idx_0848)
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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** | SupViT |
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| **Split** | train |
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| **Base Model** | `google/vit-base-patch16-224` |
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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 | 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 | 0.007 |
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| Seed | 848 |
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| Random Crop | False |
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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 | 0.9998 |
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| Val Accuracy | 0.9589 |
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| Test Accuracy | 0.9538 |
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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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`wardrobe`, `bee`, `raccoon`, `television`, `man`, `chair`, `maple_tree`, `dolphin`, `whale`, `tulip`, `pickup_truck`, `leopard`, `beaver`, `pine_tree`, `girl`, `mushroom`, `bridge`, `tiger`, `plain`, `chimpanzee`, `possum`, `bottle`, `castle`, `road`, `cockroach`, `tank`, `orange`, `lobster`, `boy`, `trout`, `cup`, `seal`, `telephone`, `train`, `can`, `pear`, `flatfish`, `crocodile`, `tractor`, `cloud`, `ray`, `skunk`, `rocket`, `bed`, `lawn_mower`, `butterfly`, `lamp`, `worm`, `woman`, `snake`
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