enginex-mlu370-any2any/transformers/docs/source/en/model_doc/swin.md

This model was released on 2021-03-25 and added to Hugging Face Transformers on 2022-01-21.

Swin Transformer

Swin Transformer is a hierarchical vision transformer. Images are processed in patches and windowed self-attention is used to capture local information. These windows are shifted across the image to allow for cross-window connections, capturing global information more efficiently. This hierarchical approach with shifted windows allows the Swin Transformer to process images effectively at different scales and achieve linear computational complexity relative to image size, making it a versatile backbone for various vision tasks like image classification and object detection.

You can find all official Swin Transformer checkpoints under the Microsoft organization.

Tip

Click on the Swin Transformer models in the right sidebar for more examples of how to apply Swin Transformer to different image tasks.

The example below demonstrates how to classify an image with [Pipeline] or the [AutoModel] class.

import torch
from transformers import pipeline

pipeline = pipeline(
    task="image-classification",
    model="microsoft/swin-tiny-patch4-window7-224",
    dtype=torch.float16,
    device=0
)
pipeline("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg")

import torch
import requests
from PIL import Image
from transformers import AutoModelForImageClassification, AutoImageProcessor

image_processor = AutoImageProcessor.from_pretrained(
    "microsoft/swin-tiny-patch4-window7-224",
    use_fast=True,
)
model = AutoModelForImageClassification.from_pretrained(
    "microsoft/swin-tiny-patch4-window7-224",
    device_map="auto"
)

device = infer_device()
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
image = Image.open(requests.get(url, stream=True).raw)
inputs = image_processor(image, return_tensors="pt").to(model.device)

with torch.no_grad():
  logits = model(**inputs).logits
predicted_class_id = logits.argmax(dim=-1).item()

class_labels = model.config.id2label
predicted_class_label = class_labels[predicted_class_id]
print(f"The predicted class label is: {predicted_class_label}")

Notes

• Swin can pad the inputs for any input height and width divisible by 32.
• Swin can be used as a backbone. When output_hidden_states = True, it outputs both hidden_states and reshaped_hidden_states. The reshaped_hidden_states have a shape of (batch, num_channels, height, width) rather than (batch_size, sequence_length, num_channels).

SwinConfig

autodoc SwinConfig

SwinModel

autodoc SwinModel - forward

SwinForMaskedImageModeling

autodoc SwinForMaskedImageModeling - forward

SwinForImageClassification

autodoc transformers.SwinForImageClassification - forward