If you are venturing into the exciting world of image classification using the MaxViT model, you’re in the right place! This guide will walk you through the steps of utilizing the MaxViT architecture, specifically the maxvit_large_tf_384.in21k_ft_in1k variant, fine-tuned on ImageNet-1k, and pretrained on ImageNet-21k. So, let’s dive in!

Understanding the Model Architecture

The MaxViT family includes various models that innovate on classic convolutional neural networks (CNNs) and transformers. To help you grasp the complexity, let’s use an analogy: imagine you’re preparing a feast. Just like how a chef combines different cooking techniques—like frying, steaming, and baking—MaxViT employs a mix of convolutional blocks and attention mechanisms. Different models within the MaxViT family utilize unique arrangements of these techniques to accommodate various dish sizes (or image sizes) without compromising on flavor (or performance).

Setting Up Your Environment

Before you can start using the MaxViT model, you’ll need to make sure you have the following setup:

• Python installed on your machine (version 3.6 or higher).
• Required libraries: Install torch and timm.
• Access to a running Jupyter notebook or a Python script editor.

Model Usage

To utilize the model for image classification, follow these steps:

Image Classification

Here’s how to classify images using the MaxViT model:

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

img = Image.open(urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'))
model = timm.create_model('maxvit_large_tf_384.in21k_ft_in1k', pretrained=True)
model = model.eval()

# 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)

Extracting Feature Maps

If you’re interested in extracting feature maps, here’s how:

model = timm.create_model('maxvit_large_tf_384.in21k_ft_in1k', pretrained=True, features_only=True)
model = model.eval()

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

Image Embeddings

In case you need to extract image embeddings, you can do it easily:

model = timm.create_model('maxvit_large_tf_384.in21k_ft_in1k', pretrained=True, num_classes=0)  # remove classifier
model = model.eval()

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

Troubleshooting

While working with deep learning models like MaxViT, you may encounter some issues. Here are some common troubleshooting tips:

• Model Not Found Error: Ensure that the model name is correctly spelled and that you are connected to the internet, as the model will be downloaded from the timm repository.
• Image Loading Issues: Verify the URL used for image loading is correct and that the image is accessible.
• Insufficient Memory: If you encounter memory errors, try reducing the image size or using a less complex model variant.

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Conclusion

With this guidance, you should now be equipped to start classifying images using the MaxViT model. As you continue your exploration, remember that the AI landscape is ever-evolving, and mastering these tools can open doors to fascinating opportunities.

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.