Create README.md (#3)

- Create README.md (ffeb9c458072e352cca2ebcf330a9a10c5c1c852)

README.md

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+# Category Search from External Databases (CaSED)
+
+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).
+
+## Intended uses & limitations
+
+You can use the model for vocabulary-free image classification, i.e. classification with CLIP-like models without a pre-defined list of class names.
+
+## How to use
+
+Here is how to use this model:
+
+```python
+import requests
+from PIL import Image
+from transformers import AutoModel, CLIPProcessor
+
+# download an image from the internet
+url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
+
+# load the model and the processor
+model = AutoModel.from_pretrained("altndrr/cased", trust_remote_code=True)
+processor = CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14")
+
+# get the model outputs
+images = processor(images=[image], return_tensors="pt", padding=True)
+outputs = model(images, alpha=0.5)
+labels, scores = outputs["vocabularies"][0], outputs["scores"][0]
+
+# print the top 5 most likely labels for the image
+values, indices = scores.topk(5)
+print("\nTop predictions:\n")
+for value, index in zip(values, indices):
+    print(f"{labels[index]:>16s}: {100 * value.item():.2f}%")
+```
+
+## Citation
+
+```latex
+@misc{conti2023vocabularyfree,
+      title={Vocabulary-free Image Classification}, 
+      author={Alessandro Conti and Enrico Fini and Massimiliano Mancini and Paolo Rota and Yiming Wang and Elisa Ricci},
+      year={2023},
+      eprint={2306.00917},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```