The Hiera image classification model is a robust tool for analyzing visual data, particularly trained on the ImageNet-1k dataset. This article will guide you through the process of using this model for image classification, feature map extraction, and image embeddings, while also providing troubleshooting tips for any challenges you may encounter.

Model Overview

The Hiera model operates based on Self-Supervised Masked Autoencoder (MAE) techniques, offering efficient image classification. Below are some critical details:

• Model Type: Image classification feature backbone
• Parameters: 51.5M
• GMACs: 9.4
• Activations: 30.4M
• Image Size: 224 x 224
• Datasets: ImageNet-1k

Getting Started with Image Classification

To start classifying images with the Hiera model, follow these steps:

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

# Load and prepare the image
img = Image.open(urlopen("https:huggingface.codatasetshuggingfacedocumentation-imagesresolvemainbeignets-task-guide.png"))
model = timm.create_model("hiera_base_224.mae_in1k_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)

# Make predictions
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)

Understanding the Code: An Analogy

Think of using the Hiera model like sending a package through a specialized delivery service. Here’s how it works:

• Loading the Image: Imagine you have a fragile package (the image) that needs to be protected and handled carefully. The urlopen function ensures the package is received from its source without damage.
• Preparing the Model: Before sending your package, you set it up with the right carrier (the model). The timm.create_model function acts as your courier, ready to transport the package safely.
• Transferring the Package: The model transforms the package (image) to fit the delivery requirements (normalization and resizing), ensuring it meets the standards of the postal service.
• Receiving the Results: Finally, the package’s delivery status is checked using torch.topk, giving you the top results to understand where your package landed.

Feature Map Extraction

To extract feature maps from the images processed by Hiera, you can follow similar steps as above:

model = timm.create_model("hiera_base_224.mae_in1k_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)  # Print shape of feature map outputs

Generating Image Embeddings

For generating embeddings from the model, use the following code:

model = timm.create_model("hiera_base_224.mae_in1k_ft_in1k", pretrained=True, num_classes=0)
model = model.eval()

output = model(transforms(img).unsqueeze(0))  # Output is (batch_size, num_features) shaped tensor
output = model.forward_features(transforms(img).unsqueeze(0))  # Output is unpooled
output = model.forward_head(output, pre_logits=True)  # Output is (1, num_features) tensor

Troubleshooting

Here are some common issues you might encounter and how to resolve them:

• If you’re receiving errors related to missing packages, ensure that all required libraries are properly installed, such as torch and timm.
• If the images are not loading, verify the image URL is correct and accessible.
• If the model predictions seem inaccurate, consider re-checking the input image size and that your model has been set to evaluation mode with model.eval().

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Conclusion

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.