The Swin Transformer model is a versatile and powerful tool for image classification tasks, pretrained on a vast dataset and fine-tuned for specific applications. In this article, we will show you how to use this model for image classification, extract feature maps, and obtain image embeddings. Buckle up as we dive into the world of Swin Transformers!

Model Overview

The Swin Transformer model, particularly swin_base_patch4_window7_224.ms_in22k_ft_in1k, functions as a backbone for image classification. Here’s a snapshot of its core details:

• Model Type: Image classification feature backbone
• Parameters: 87.8 million
• GMACs: 15.5
• Activations: 36.6 million
• Image Size: 224 x 224

For more detailed reading, you can refer to the original paper, Swin Transformer: Hierarchical Vision Transformer using Shifted Windows, and the original GitHub repository.

How to Use the Model

Now, let’s cover how to implement the model for various tasks:

1. Image Classification

To classify an image, utilize the following Python snippet:

python
from urllib.request import urlopen
from PIL import Image
import timm

img = Image.open(urlopen("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png"))
model = timm.create_model('swin_base_patch4_window7_224.ms_in22k_ft_in1k', pretrained=True)
model = model.eval()  # Set to evaluation mode

# Get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)

output = model(transforms(img).unsqueeze(0))  # Unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)

This snippet functions like a chef preparing a wide range of dishes from the same set of ingredients. Here, the image is the ingredient; you apply various operations (transforms and model evaluation) to extract the final classifications, just like you would select and combine spices to elevate each dish.

2. Feature Map Extraction

If you’re looking to extract feature maps from the model, here’s how:

python
from urllib.request import urlopen
from PIL import Image
import timm

img = Image.open(urlopen("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png"))
model = timm.create_model('swin_base_patch4_window7_224.ms_in22k_ft_in1k', pretrained=True, features_only=True)
model = model.eval()  # Set to evaluation mode

# Get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)

output = model(transforms(img).unsqueeze(0))  # Unsqueeze single image into batch of 1
for o in output:
    print(o.shape)  # Print shape of each feature map in output

Feature map extraction is akin to a detective analyzing clues at a crime scene. Each feature map reveals different levels or dimensions of the image, helping you understand its structure from simple to complex associations.

3. Image Embeddings

For obtaining image embeddings, use the following code:

python
from urllib.request import urlopen
from PIL import Image
import timm

img = Image.open(urlopen("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png"))
model = timm.create_model('swin_base_patch4_window7_224.ms_in22k_ft_in1k', pretrained=True, num_classes=0)  # Remove classifier
model = model.eval()  # Set to evaluation mode

# Get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)

output = model(transforms(img).unsqueeze(0))  # Output is (batch_size, num_features) shaped tensor
output = model.forward_features(transforms(img).unsqueeze(0))  # Unpooled features

Obtaining image embeddings can be likened to extracting the essence of a story from a novel. You are distilling the rich details into a compact format (representative features) that encapsulates the image’s most vital information.

Troubleshooting

Should you encounter any hiccups during implementation, consider the following troubleshooting tips:

• Ensure that the required libraries such as timem, PIL, and torch are properly installed.
• Double-check that the internet connection is stable for fetching images from URLs.
• If you experience issues with model loading, verify that the model name is correctly specified.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

In summary, the Swin Transformer model empowers you to effectively classify images, extract valuable features, and obtain embeddings seamlessly. At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.