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| import torch | |
| from transformers import AutoFeatureExtractor, AutoModelForImageClassification | |
| from einops import rearrange | |
| import gradio | |
| import call_labels | |
| # define the feature extractor | |
| extractor = AutoFeatureExtractor.from_pretrained("vincentclaes/mit-indoor-scenes") | |
| # define the pretrained model | |
| model = AutoModelForImageClassification.from_pretrained("vincentclaes/mit-indoor-scenes") | |
| # retrieve the labels provided from MIT Indoor Scenes dataset (https://www.kaggle.com/itsahmad/indoor-scenes-cvpr-2019) | |
| labels = call_labels.call_labels() | |
| # call model.eval() to assert that we are evaluating the model and not updating the weights | |
| model.eval() | |
| # define the function used for model inference | |
| def classify(image): | |
| # disable gradient calculation | |
| with torch.no_grad(): | |
| # extract features from the image input | |
| inputs = extractor(images=image, return_tensors='pt') | |
| # call the logits parameter only (object: SequenceClassifierOutput) | |
| outputs = model(**inputs).logits | |
| # remove the batch dimension | |
| outputs = rearrange(outputs, '1 j->j') | |
| # use the softmax function to convert the logits into probabilities | |
| outputs = torch.nn.functional.softmax(outputs) | |
| # convert the data type from tensor to a numpy array | |
| outputs = outputs.cpu().numpy() | |
| # returns a key-value pair of the id labels and its corresponding probabilities | |
| return {labels[str(i)]: float(outputs[i]) for i in range(len(labels))} | |
| # define the gradio interface | |
| gradio.Interface(fn=classify, | |
| inputs=gradio.inputs.Image(shape=(224,224), | |
| image_mode='RGB', | |
| source='upload', | |
| tool='editor', | |
| type='pil', | |
| label=None, | |
| optional=False), | |
| outputs=gradio.outputs.Label(num_top_classes=5, | |
| type='auto'), | |
| theme='grass', | |
| examples=[['bedroom.jpg'], | |
| ['cafe_shop.jpg'], | |
| ['samsung_room.jpg']], | |
| live=True, | |
| layout='horizontal', | |
| title='Indoor Scene Recognition', | |
| description='A smart and easy-to-use indoor scene classifier. Start by uploading an input image. The outputs are the top five indoor scene classes that best fit your input image.', | |
| article='''<h2>Additional Information</h2><p style='text-align: justify'>This indoor scene classifier employs the <b><a href='https://huggingface.co/google/vit-base-patch16-224-in21k' target='_blank'>google/vit-base-patch16-224-in21k</a></b>, a <b>Visual Transformer (ViT)</b> model pre-trained on <b><a href='https://github.com/Alibaba-MIIL/ImageNet21K' target='_blank'>ImageNet-21k</a></b> (14 million images, 21,843 classes) at a resolution of 224 pixels by 224 pixels and was first introduced in the paper <b><a href='https://arxiv.org/abs/2010.11929' target='_blank'>An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale</a></b> by Dosovitskiy et al. It was then fine-tuned on the <b><a href='https://www.kaggle.com/itsahmad/indoor-scenes-cvpr-2019' target='_blank'>MIT Indoor Scenes</a></b> data set from Kaggle. The source model used in this space is from <b><a href='https://huggingface.co/vincentclaes/mit-indoor-scenes' target='_blank'>vincentclaes/mit-indoor-scenes</a></b>.</p> | |
| <p style='text-align: justify'>For further research on the Visual Transformer, the original GitHub repository is found in <b><a href='https://github.com/google-research/vision_transformer' target='_blank'>this link</a></b>.</p> | |
| <h2>Disclaimer</h2> | |
| <p style='text-align: justify'>The team releasing the Visual Transformer did not write a model card for it via Hugging Face. Hence, the Visual Transformer model card released in the Hugging Face Models library has been written by the Hugging Face team.</p>''', | |
| allow_flagging='never').launch() |