In the evolving world of artificial intelligence, the ability to classify images accurately has become crucial. With models like MaxViT, developed specifically for this task, machine learning becomes a powerful tool. In this guide, we’ll take a step-by-step approach to using the MaxViT model for image classification, feature extraction, and generating image embeddings. Let’s dive in!

Getting Started with MaxViT

The MaxViT model, which has been pretrained on ImageNet-21k and fine-tuned on ImageNet-1k, allows for impressive accuracy in classifying images. Before you begin, ensure that you have the necessary libraries installed:

• Python
• Pillow (for image processing)
• Timm (PyTorch Image Models)

Image Classification

To classify an image, you need to follow these steps:

• Import the required libraries.
• Load the image you want to classify.
• Initialize the MaxViT model.
• Process the image through the model.

Here’s how to execute these steps in code:

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("maxvit_base_tf_512.in21k_ft_in1k", pretrained=True)
model = model.eval()
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)

In the above code:

• We import necessary modules to handle images and the model.
• The image is retrieved via a URL and opened using Pillow.
• The MaxViT model is created and set to evaluation mode.
• We configure data transformations to prepare the image for the model.
• Finally, we execute the model on the image and retrieve the top 5 classification results.

Feature Map Extraction

Feature maps can help us understand the layers of information the model processed from the image. To extract feature maps, follow these steps:

• Load the image as before.
• Initialize the model for feature extraction.
• Pass the image through the model to retrieve features.

This is how it’s done in code:

model = timm.create_model("maxvit_base_tf_512.in21k_ft_in1k", pretrained=True, features_only=True)
model = model.eval()
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)

Similar to image classification, we load the model but set the features_only parameter to True. This allows us to extract detailed information from the images.

Generating Image Embeddings

To obtain compact feature representations called embeddings from images, the process is similar:

• Load the image again.
• Set the model to return features without any classification head.
• Run the model to get the final embeddings.

Here’s how:

model = timm.create_model("maxvit_base_tf_512.in21k_ft_in1k", pretrained=True, num_classes=0)  # remove classifier
model = model.eval()
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

Here, we set num_classes=0 to extract features without the final linear classification layer.

Troubleshooting Common Issues

As with any machine learning project, challenges may arise. Here are some common troubleshooting tips:

• Check your Python Environment: Ensure that you have installed all necessary libraries.
• Model Loading Errors: Ensure you are using the correct model name and it is available for download.
• Image Processing Errors: Make sure the image URL is valid and the image is in a supported format.
• Memory Issues: If your system runs out of memory, consider using a smaller model variant.

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Conclusion

By following these steps, you can effectively utilize the MaxViT model for image classification tasks, extract valuable features from images, and generate embeddings for various applications. Whether you’re working on a research project or developing applications, embracing powerful models like MaxViT will significantly enhance your capability to handle image data.

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.