The Swin Transformer model, specifically designed for image classification, offers a robust method for processing visual data. In this guide, we will walk through the details of using this model for image classification, feature map extraction, and obtaining image embeddings.
Model Overview
The Swin Transformer is an innovative image classification backbone characterized by the following statistics:
- Parameters: 71.1M
- GMACs: 13.7
- Activations: 48.3M
- Image Size: 224 x 224
For detailed research, refer to the following papers:
The model works with the ImageNet-1k dataset and is accessible via the timm library in Python.
Getting Started with Image Classification
To classify images using the Swin Transformer model, follow the steps below:
python
from urllib.request import urlopen
from PIL import Image
import timm
# Load image
img = Image.open(urlopen("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png"))
# Create model
model = timm.create_model("swin_s3_base_224.ms_in1k", pretrained=True)
model = model.eval()
# Get model specific transforms
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
# Forward pass
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)
Feature Map Extraction
Feature maps provide a detailed view of the layers of a neural network. Using the Swin Transformer to extract these maps involves a slight modification:
python
# Load model for feature extraction
model = timm.create_model("swin_s3_base_224.ms_in1k", pretrained=True, features_only=True)
model = model.eval()
# Forward pass to get feature maps
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
Obtaining Image Embeddings
Image embeddings are compact representations of the images typically used for various downstream tasks. Here’s how to get embeddings from the Swin Transformer:
python
# Load model for embeddings
model = timm.create_model("swin_s3_base_224.ms_in1k", pretrained=True, num_classes=0) # remove classifier
model = model.eval()
# Forward features to get embeddings
output = model(transforms(img).unsqueeze(0)) # outputs shape: (batch_size, num_features)
output = model.forward_features(transforms(img).unsqueeze(0))
output = model.forward_head(output, pre_logits=True) # output is (batch_size, num_features) tensor
Troubleshooting Common Errors
While implementing this model, you may encounter some common issues. Here are potential fixes:
- Error: ModuleNotFoundError – Ensure that you have the timm and PIL libraries installed in your Python environment.
- Error: Incorrect Image Format – Verify that the input image is loaded correctly and the format is supported by PIL.
- Unresponsive Model – If the model hangs or crashes, check your available system memory and responsible CPU/GPU usage.
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Conclusion
By following this guide, you should now be able to use the Swin Transformer for image classification, feature map extraction, and obtaining effective image embeddings. 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.