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license: apache-2.0 |
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# ViT Fine-tuned on Stanford Car Dataset |
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Base model: https://huggingface.co/google/vit-base-patch16-224 |
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This achieves around 86% on the testing set, you can use it as a baseline for further tuning. |
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# Dataset Description |
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The Stanford car dataset contains 16,185 images of 196 classes of cars. Classes are typically at the level of Make, Model, Year, e.g. 2012 Tesla Model S or 2012 BMW M3 coupe. The data is split into 8144 training images, 6,041 testing images, and 2000 validation images in this case. |
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** Please note: this dataset does not contain newer car models ** |
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# Using the Model in the Transformer Library |
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``` |
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from transformers import AutoFeatureExtractor, AutoModelForImageClassification |
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extractor = AutoFeatureExtractor.from_pretrained("therealcyberlord/stanford-car-vit-patch16") |
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model = AutoModelForImageClassification.from_pretrained("therealcyberlord/stanford-car-vit-patch16") |
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``` |
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# Citations |
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3D Object Representations for Fine-Grained Categorization |
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Jonathan Krause, Michael Stark, Jia Deng, Li Fei-Fei |
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4th IEEE Workshop on 3D Representation and Recognition, at ICCV 2013 (3dRR-13). Sydney, Australia. Dec. 8, 2013. |
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