The world of image classification is ever-evolving, and one of the latest titans in this realm is the ConvNeXt_Pico model, designed by Ross Wightman. In this article, we’ll go step-by-step on how to use this state-of-the-art model to classify images, extract feature maps, and generate embeddings!

Model Overview

The ConvNeXt_Pico model, trained on the ImageNet-1k dataset, serves as an efficient backbone for image classification tasks. Below are some important details:

• Model Type: Image classification feature backbone
• Parameters: 9.0 million
• GMACs: 1.4
• Image Size: Training – 224 x 224, Testing – 288 x 288
• Papers: A ConvNet for the 2020s
• Original Code: GitHub Repository

Steps for Image Classification

1. Setup Your Environment

Make sure you have the necessary libraries installed. You will need timm and PIL. If you haven’t, install them using:

pip install timm pillow

2. Load a Sample Image

First, let’s load an image that we will classify. You can use any URL for an image or even a local file path:

from urllib.request import urlopen
from PIL import Image

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

3. Load the ConvNeXt_Pico Model

Now, let’s create the model:

import timm

model = timm.create_model("convnext_pico.d1_in1k", pretrained=True)
model = model.eval()

4. Prepare Data Transformations

Now, we need to preprocess the image to match the model’s expected input format:

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))  # Prepare input

5. Get Predictions

Finally, we can obtain the top predictions from the model:

top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)

Feature Map Extraction

If you’re interested in the internal workings of the model, you can also extract feature maps:

model = timm.create_model("convnext_pico.d1_in1k", pretrained=True, features_only=True)
output = model(transforms(img).unsqueeze(0))

for o in output:
    print(o.shape)  # Each shape corresponds to different layers

Generating Image Embeddings

Generating embeddings can be useful for various applications like clustering images or feeding them into other machine learning algorithms:

model = timm.create_model("convnext_pico.d1_in1k", pretrained=True, num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))

Troubleshooting Common Issues

• Issue: Import Error
• Solution: Ensure that you have installed all required libraries. Use the installation command mentioned in Step 1.
• Issue: Incorrect Image Format
• Solution: Ensure that the image URL is accessible and the file format is supported (e.g., PNG, JPEG).
• Issue: Model Not Found
• Solution: Double-check the model name to ensure correctness: convnext_pico.d1_in1k.
• Issue: Runtime Errors
• Solution: If you encounter any runtime issues, make sure your runtime environment is adequately set up. Consider using virtual environments to avoid package conflicts.

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Conclusion

Now you are equipped with the necessary steps to utilize the ConvNeXt_Pico model for image classification, feature extraction, and embeddings generation. With its efficient architecture, this model opens new avenues for AI development.

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.