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README.md
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---
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base_model: facebook/dino-vitb16
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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: DINO Model (model_idx_0281)
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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** | DINO |
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| **Split** | train |
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| **Base Model** | `facebook/dino-vitb16` |
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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 | 0.0003 |
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| LR Scheduler | constant_with_warmup |
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| Epochs | 2 |
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| Max Train Steps | 666 |
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| Batch Size | 64 |
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| Weight Decay | 0.05 |
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| Seed | 281 |
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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 | 0.2513 |
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| Val Accuracy | 0.2344 |
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| Test Accuracy | 0.2452 |
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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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`wolf`, `rose`, `whale`, `mushroom`, `lamp`, `lizard`, `poppy`, `mountain`, `possum`, `butterfly`, `tank`, `beetle`, `otter`, `seal`, `caterpillar`, `shark`, `bee`, `pine_tree`, `bottle`, `aquarium_fish`, `keyboard`, `bowl`, `oak_tree`, `clock`, `raccoon`, `plain`, `telephone`, `cloud`, `television`, `pickup_truck`, `table`, `streetcar`, `couch`, `willow_tree`, `tulip`, `bicycle`, `spider`, `flatfish`, `bed`, `porcupine`, `bridge`, `lawn_mower`, `baby`, `turtle`, `fox`, `pear`, `snail`, `dinosaur`, `cockroach`, `rocket`
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