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Update README

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  1. README.md +9 -10
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@@ -1,12 +1,13 @@
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  ---
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  pipeline_tag: image-classification
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  tags:
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- - vision
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  inference: false
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  widget:
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- - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-dog-music.png
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- example_title: Cat & Dog
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  ---
 
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  # Category Search from External Databases (CaSED)
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  Disclaimer: The model card is taken and modified from the official repository, which can be found [here](https://github.com/altndrr/vic). The paper can be found [here](https://arxiv.org/abs/2306.00917).
@@ -34,7 +35,7 @@ processor = CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14")
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  # get the model outputs
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  images = processor(images=[image], return_tensors="pt", padding=True)
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- outputs = model(images, alpha=0.5)
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  labels, scores = outputs["vocabularies"][0], outputs["scores"][0]
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  # print the top 5 most likely labels for the image
@@ -47,18 +48,16 @@ for value, index in zip(values, indices):
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  The model depends on some libraries you have to install manually before execution:
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  ```bash
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- pip install torch faiss-cpu flair inflect nltk transformers
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  ```
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  ## Citation
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  ```latex
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- @misc{conti2023vocabularyfree,
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  title={Vocabulary-free Image Classification},
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  author={Alessandro Conti and Enrico Fini and Massimiliano Mancini and Paolo Rota and Yiming Wang and Elisa Ricci},
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  year={2023},
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- eprint={2306.00917},
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- archivePrefix={arXiv},
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- primaryClass={cs.CV}
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  }
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- ```
 
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  ---
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  pipeline_tag: image-classification
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  tags:
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+ - vision
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  inference: false
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  widget:
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+ - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-dog-music.png
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+ example_title: Cat & Dog
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  ---
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+
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  # Category Search from External Databases (CaSED)
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  Disclaimer: The model card is taken and modified from the official repository, which can be found [here](https://github.com/altndrr/vic). The paper can be found [here](https://arxiv.org/abs/2306.00917).
 
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  # get the model outputs
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  images = processor(images=[image], return_tensors="pt", padding=True)
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+ outputs = model(images, alpha=0.7)
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  labels, scores = outputs["vocabularies"][0], outputs["scores"][0]
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  # print the top 5 most likely labels for the image
 
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  The model depends on some libraries you have to install manually before execution:
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  ```bash
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+ pip install torch faiss-cpu flair inflect nltk pyarrow transformers
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  ```
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  ## Citation
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  ```latex
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+ @article{conti2023vocabularyfree,
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  title={Vocabulary-free Image Classification},
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  author={Alessandro Conti and Enrico Fini and Massimiliano Mancini and Paolo Rota and Yiming Wang and Elisa Ricci},
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  year={2023},
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+ journal={NeurIPS},
 
 
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  }
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+ ```