In this guide, we’ll walk you through the process of utilizing the ConvNeXt XLarge model, pretrained on the ImageNet-22k dataset, for image classification. This state-of-the-art model, developed by paper authors, boasts a powerful architecture designed for robust image classification tasks.

Model Details

• Model Type: Image classification feature backbone
• Parameters (M): 392.9
• GMACs: 61.0
• Activations (M): 57.5
• Image Size: 224 x 224
• Papers: A ConvNet for the 2020s
• Original Repo: GitHub Repository

Using the Model for Image Classification

Follow the steps below to classify images using the ConvNeXt XLarge model:

Step 1: Import Required Libraries

Begin by importing the necessary libraries to handle image processing and model execution.

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

Step 2: Load the Image

Load an image from a URL using the following code:

img = Image.open(urlopen("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png"))

Step 3: Load and Prepare the Model

Next, create and prepare the model for evaluation:

model = timm.create_model('convnext_xlarge.fb_in22k', pretrained=True)
model = model.eval()
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)

Step 4: Classify the Image

Now, classify the image using the model:

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

You can also extract feature maps from the model, which is an insightful way to understand how the model processes an image. Here’s how:

model = timm.create_model('convnext_xlarge.fb_in22k', pretrained=True, features_only=True)
model = model.eval()
output = model(transforms(img).unsqueeze(0))  # Extract feature maps
for o in output:
    print(o.shape)  # Print shape of each feature map

Generating Image Embeddings

To retrieve image embeddings, follow these instructions:

model = timm.create_model('convnext_xlarge.fb_in22k', pretrained=True, num_classes=0)  # Remove classifier
model = model.eval()
output = model(transforms(img).unsqueeze(0))  # Output is (batch_size, num_features) shaped tensor

Model Comparison

Comparing model performance can help you choose the best architecture for your needs. You can view the dataset and runtime metrics for numerous models in timm model results.

Analogy for Understanding the Code

Think of the ConvNeXt XLarge model as a high-tech restaurant chef. Each image that you input is like a dish that the chef will create. You start by selecting ingredients (the image’s pixel data) and preparing them (through transformations). The chef then uses their specialized skills (the pretrained model) to create a masterpiece (the classification output). The feature maps are the chef’s various techniques and steps during the cooking process that reveal how the dish is made. The final dish (output) is beautifully presented and includes the best possible classification (like how you would present the dish to a customer). So, you get to peek behind the curtain and understand the chef’s (model’s) process!

Troubleshooting

If you run into issues while using the model, consider the following troubleshooting steps:

• Ensure all libraries are up to date and compatible with each other.
• Check the validity of the image URL and make sure it points to a correct image format (e.g., PNG, JPEG).
• Adjust the input image size to 224 x 224 if you encounter shape errors.
• Check for any changes in the Hugging Face model repository that may affect your model usage.

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