README.md

metadata

license: apache-2.0
tags:
  - vision
  - image-classification
datasets:
  - imagenet-1k
widget:
  - src: >-
      https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
    example_title: Tiger
  - src: >-
      https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
    example_title: Teapot
  - src: >-
      https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
    example_title: Palace

DiNAT (mini variant)

WIP.

Intended uses & limitations

You can use the raw model for image classification. See the model hub 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:

from transformers import AutoFeatureExtractor, DiNATForImageClassification
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 = AutoFeatureExtractor.from_pretrained("shi-labs/dinat-tiny-in1k-224")
model = DiNATForImageClassification("shi-labs/dinat-tiny-in1k-224")

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])

For more code examples, we refer to the documentation.

BibTeX entry and citation info

@article{hassani2022dilated,
    title        = {Dilated Neighborhood Attention Transformer},
    author       = {Ali Hassani and Humphrey Shi},
    year         = 2022,
    url          = {https://arxiv.org/abs/2209.15001},
    eprint       = {2209.15001},
    archiveprefix = {arXiv},
    primaryclass = {cs.CV}
}