Welcome to your step-by-step guide on how to leverage the powerful ConvNeXt model for image classification! This model is designed to operate on images from the renowned ImageNet-1k dataset and has been meticulously designed by Ross Wightman.

Model Overview

The ConvNeXt image classification model serves as a feature backbone with impressive stats:

• Parameters: 15.6 million
• GMACs: 2.7
• Activations: 9.4 million
• Training Image Size: 224×224
• Testing Image Size: 288×288

For further details, you can explore the original research paper titled A ConvNet for the 2020s.

Using ConvNeXt for Image Classification

To use the ConvNeXt model, follow these coding steps:

Step 1: Load Your Environment

Make sure you have the necessary libraries installed in your Python environment. You will need timm and torch for this.

Step 2: Write the Code

Here’s a simplified example of how to implement the model:

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

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

# Create the model
model = timm.create_model("convnext_nano_ols.d1h_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)

# Process the image
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 this code snippet, you’re essentially setting up a cooking recipe:

• Ingredients: Import necessary libraries.
• Preparation: Load the image.
• Cooking Action: Create the model and transform the image.
• Presentation: Get the results with top 5 classifications.

Feature Map Extraction

Extracting feature maps is a vital part of understanding the classification process.

model = timm.create_model("convnext_nano_ols.d1h_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))  # Unsqueezing for batch processing

for o in output:
    print(o.shape)  # Shape of each feature map

This will print out the shape of each feature map, giving insights into the model’s workings at different layers.

Retrieving Image Embeddings

For retrieving image embeddings, you can modify the `model` as follows:

model = timm.create_model("convnext_nano_ols.d1h_in1k", pretrained=True, num_classes=0) 
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 the embeddings

In this part, you can think of the model as a skilled artist who captures the essence of the image in the form of embeddings.

Troubleshooting

If you run into issues while using the ConvNeXt model, here are some troubleshooting suggestions:

• Ensure all necessary libraries are updated to their latest versions.
• Verify that the image URL is functioning and the format is supported.
• Check if your environment supports the required dependencies for timm and torch.
• If the model doesn’t return expected results, consider experimenting with different images or configurations.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Comparison with Other Models

For a broader understanding, please explore the comparison of ConvNeXt with other models using the model results on GitHub.

Final Thoughts

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.