Create README.md

README.md

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+---
+datasets:
+- imagenet-1k
+library_name: transformers
+pipeline_tag: image-classification
+---
+
+# MobileViTv2 (mobilevitv2-1.0-imagenet1k-256)
+
+<!-- Provide a quick summary of what the model is/does. -->
+MobileViTv2 is the second version of MobileViT. It was proposed in [Separable Self-attention for Mobile Vision Transformers](https://arxiv.org/abs/2206.02680) by Sachin Mehta and Mohammad Rastegari, and first released in [this](https://github.com/apple/ml-cvnets) repository. The license used is [Apple sample code license](https://github.com/apple/ml-cvnets/blob/main/LICENSE).
+
+Disclaimer: The team releasing MobileViT did not write a model card for this model so this model card has been written by the Hugging Face team.
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+MobileViTv2 is constructed by replacing the multi-headed self-attention in MobileViT with separable self-attention.
+
+### Intended uses & limitations
+
+You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=mobilevitv2) to look for fine-tuned versions on a task that interests you.
+
+### How to use
+
+Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
+
+```python
+from transformers import MobileViTv2FeatureExtractor, MobileViTv2ForImageClassification
+from PIL import Image
+import requests
+
+url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
+
+feature_extractor = MobileViTv2FeatureExtractor.from_pretrained("shehan97/mobilevitv2-1.0-imagenet1k-256")
+model = MobileViTv2ForImageClassification.from_pretrained("shehan97/mobilevitv2-1.0-imagenet1k-256")
+
+inputs = feature_extractor(images=image, return_tensors="pt")
+
+outputs = model(**inputs)
+logits = outputs.logits
+
+# model predicts one of the 1000 ImageNet classes
+predicted_class_idx = logits.argmax(-1).item()
+print("Predicted class:", model.config.id2label[predicted_class_idx])
+```
+
+Currently, both the feature extractor and model support PyTorch.
+
+## Training data
+
+The MobileViT model was pretrained on [ImageNet-1k](https://huggingface.co/datasets/imagenet-1k), a dataset consisting of 1 million images and 1,000 classes. 
+
+
+### BibTeX entry and citation info
+
+```bibtex
+@inproceedings{vision-transformer,
+title = {Separable Self-attention for Mobile Vision Transformers},
+author = {Sachin Mehta and Mohammad Rastegari},
+year = {2022},
+URL = {https://arxiv.org/abs/2206.02680}
+}
+```