Nat Small In1k 224

🚀 NAT (small variant)

NAT-Small is a vision model trained on ImageNet-1K at a 224x224 resolution. It offers efficient image classification capabilities based on the Neighborhood Attention mechanism.

🚀 Quick Start

You can use the raw model for image classification. Check out the model hub to find fine - tuned versions for tasks that interest you.

Example

Here's how to classify an image from the COCO 2017 dataset into one of the 1,000 ImageNet classes using this model:

from transformers import AutoImageProcessor, NatForImageClassification
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 = AutoImageProcessor.from_pretrained("shi-labs/nat-small-in1k-224")
model = NatForImageClassification.from_pretrained("shi-labs/nat-small-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 examples, refer to the documentation.

Requirements

Apart from transformers, this model requires the NATTEN package.

• If you're using Linux, you can refer to shi-labs.com/natten for instructions on installing with pre - compiled binaries (just select your torch build to get the correct wheel URL).
• You can also use pip install natten to compile on your device, which may take up to a few minutes. Mac users only have this option (no pre - compiled binaries). Refer to [NATTEN's GitHub](https://github.com/SHI - Labs/NATTEN/) for more information.

✨ Features

• Hierarchical Vision Transformer: NAT is a hierarchical vision transformer based on Neighborhood Attention (NA).
• Restricted Self - Attention: Neighborhood Attention restricts each token's receptive field to its nearest neighboring pixels, offering a sliding - window attention pattern.
• Flexibility and Translational Equivariance: NA is highly flexible and maintains translational equivariance.
• PyTorch Implementation: NA is implemented in PyTorch through its extension, [NATTEN](https://github.com/SHI - Labs/NATTEN/).

📚 Documentation

Model description

NAT is a hierarchical vision transformer based on Neighborhood Attention (NA). Neighborhood Attention is a restricted self - attention pattern where each token's receptive field is limited to its nearest neighboring pixels. NA is a sliding - window attention pattern, making it highly flexible and maintaining translational equivariance.

NA is implemented in PyTorch implementations through its extension, [NATTEN](https://github.com/SHI - Labs/NATTEN/).

Source

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.

BibTeX entry and citation info

@article{hassani2022neighborhood,
	title        = {Neighborhood Attention Transformer},
	author       = {Ali Hassani and Steven Walton and Jiachen Li and Shen Li and Humphrey Shi},
	year         = 2022,
	url          = {https://arxiv.org/abs/2204.07143},
	eprint       = {2204.07143},
	archiveprefix = {arXiv},
	primaryclass = {cs.CV}
}

📄 License

This project is licensed under the MIT license.

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