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--- |
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license: apache-2.0 |
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tags: |
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- vision |
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- image-classification |
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datasets: |
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- imagenet-1k |
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widget: |
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- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg |
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example_title: Tiger |
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- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg |
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example_title: Teapot |
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- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg |
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example_title: Palace |
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--- |
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# DiNAT (mini variant) |
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WIP. |
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## Intended uses & limitations |
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You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=dinat) to look for |
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fine-tuned versions on a task that interests you. |
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### How to use |
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Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: |
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```python |
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from transformers import AutoFeatureExtractor, DiNATForImageClassification |
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from PIL import Image |
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import requests |
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url = "http://images.cocodataset.org/val2017/000000039769.jpg" |
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image = Image.open(requests.get(url, stream=True).raw) |
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feature_extractor = AutoFeatureExtractor.from_pretrained("shi-labs/dinat-tiny-in1k-224") |
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model = DiNATForImageClassification("shi-labs/dinat-tiny-in1k-224") |
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inputs = feature_extractor(images=image, return_tensors="pt") |
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outputs = model(**inputs) |
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logits = outputs.logits |
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# model predicts one of the 1000 ImageNet classes |
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predicted_class_idx = logits.argmax(-1).item() |
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print("Predicted class:", model.config.id2label[predicted_class_idx]) |
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``` |
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For more code examples, we refer to the [documentation](https://huggingface.co/transformers/model_doc/dinat.html#). |
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### BibTeX entry and citation info |
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```bibtex |
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@article{hassani2022dilated, |
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title = {Dilated Neighborhood Attention Transformer}, |
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author = {Ali Hassani and Humphrey Shi}, |
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year = 2022, |
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url = {https://arxiv.org/abs/2209.15001}, |
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eprint = {2209.15001}, |
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archiveprefix = {arXiv}, |
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primaryclass = {cs.CV} |
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} |
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``` |