This model was released on 2021-11-18 and added to Hugging Face Transformers on 2022-07-27.

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Swin Transformer V2

Swin Transformer V2 is a 3B parameter model that focuses on how to scale a vision model to billions of parameters. It introduces techniques like residual-post-norm combined with cosine attention for improved training stability, log-spaced continuous position bias to better handle varying image resolutions between pre-training and fine-tuning, and a new pre-training method (SimMIM) to reduce the need for large amounts of labeled data. These improvements enable efficiently training very large models (up to 3 billion parameters) capable of processing high-resolution images.

You can find official Swin Transformer V2 checkpoints under the Microsoft organization.

[!TIP] Click on the Swin Transformer V2 models in the right sidebar for more examples of how to apply Swin Transformer V2 to vision tasks.

from transformers import pipeline


pipeline = pipeline(
    task="image-classification",
    model="microsoft/swinv2-tiny-patch4-window8-256",
    device=0
)
pipeline("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg")

import requests
import torch
from PIL import Image

from transformers import AutoImageProcessor, AutoModelForImageClassification


image_processor = AutoImageProcessor.from_pretrained(
    "microsoft/swinv2-tiny-patch4-window8-256",
)
model = AutoModelForImageClassification.from_pretrained(
    "microsoft/swinv2-tiny-patch4-window8-256",
    device_map="auto"
)

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

Notes

• Swin Transformer V2 can pad the inputs for any input height and width divisible by 32.
• Swin Transformer V2 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).

Swinv2Config

[[autodoc]] Swinv2Config

Swinv2Model

[[autodoc]] Swinv2Model - forward

Swinv2ForMaskedImageModeling

[[autodoc]] Swinv2ForMaskedImageModeling - forward

Swinv2ForImageClassification

[[autodoc]] transformers.Swinv2ForImageClassification - forward