nateraw
:pencil: edit README.md 9010034
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---
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tags:
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- image-classification
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- pytorch
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license: apache-2.0
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datasets:
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- cifar10
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metrics:
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- accuracy
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thumbnail: https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4
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---
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# Vision Transformer Fine Tuned on CIFAR10
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Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) and **fine-tuned on CIFAR10** at resolution 224x224.
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Check out the code at my [my Github repo](https://github.com/nateraw/huggingface-vit-finetune).
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## Usage
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```python
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from transformers import ViTFeatureExtractor, ViTForImageClassification
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from PIL import Image
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import requests
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url = 'https://www.cs.toronto.edu/~kriz/cifar-10-sample/dog10.png'
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image = Image.open(requests.get(url, stream=True).raw)
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feature_extractor = ViTFeatureExtractor.from_pretrained('nateraw/vit-base-patch16-224-cifar10')
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model = ViTForImageClassification.from_pretrained('nateraw/vit-base-patch16-224-cifar10')
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inputs = feature_extractor(images=image, return_tensors="pt")
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outputs = model(**inputs)
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preds = outputs.logits.argmax(dim=1)
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classes = [
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    'airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck'
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]
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classes[preds[0]]
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```
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## Model description
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The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. 
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Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.
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Note that this model does not provide any fine-tuned heads, as these were zero'd by Google researchers. However, the model does include the pre-trained pooler, which can be used for downstream tasks (such as image classification).
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By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.
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