The ConvNeXt-V2 is a cutting-edge image classification model that combines advanced training techniques with a variety of datasets. In this guide, we’ll walk through how to use this model for various tasks including image classification, feature map extraction, and image embeddings.

Model Overview

The ConvNeXt-V2 image classification model is pretrained with a fully convolutional masked autoencoder framework (FCMAE) and fine-tuned on ImageNet-22k and then ImageNet-1k. It boasts impressive stats:

• Parameters: 660.3M
• GMACs: 338.0
• Activations: 232.4M
• Image Size: 384 x 384

1. Image Classification

Let’s start with image classification. Imagine you’re a detective analyzing a collection of photos. You need to identify which objects are in each picture swiftly. That’s what ConvNeXt-V2 can do for images!

Here’s how to perform image classification:

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

img = Image.open(urlopen('https:huggingface.codatasetshuggingfacedocumentation-imagesresolvemainbeignets-task-guide.png'))
model = timm.create_model('convnextv2_huge.fcmae_ft_in22k_in1k_384', 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)

2. Feature Map Extraction

After identifying the objects, perhaps you want to dive deeper and understand how the model interprets images. This is akin to examining the detective’s notes for clues. Here’s how to extract feature maps:

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

img = Image.open(urlopen('https:huggingface.codatasetshuggingfacedocumentation-imagesresolvemainbeignets-task-guide.png'))
model = timm.create_model('convnextv2_huge.fcmae_ft_in22k_in1k_384', pretrained=True, features_only=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

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

3. Image Embeddings

If you’re interested in representing images as numerical vectors for further analysis or comparisons, you can obtain image embeddings. In this scenario, envision converting each detective’s notes into a short summary. Here’s how:

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

img = Image.open(urlopen('https:huggingface.codatasetshuggingfacedocumentation-imagesresolvemainbeignets-task-guide.png'))
model = timm.create_model('convnextv2_huge.fcmae_ft_in22k_in1k_384', pretrained=True, num_classes=0)  # Remove classifier
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))  # Output is (batch_size, num_features) shaped tensor

Troubleshooting

If you encounter any problems while using the ConvNeXt-V2 model, consider the following:

• Ensure you have the required packages: Verify that you have installed the timm, torch, and PIL (Pillow) packages.
• Check the image URL: If your input image isn’t loading, ensure that the URL is correct.
• Model not found error: Confirm you spelled the model name correctly and that you have internet access to download the pretrained weights.
• Performance issues: Monitor your GPU memory usage and consider using smaller image sizes or batch sizes.

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

Conclusion

By following this guide, you’ve equipped yourself with the knowledge to harness the power of the ConvNeXt-V2 for image classification, feature extraction, and embeddings. As you explore further, remember: each image holds a story—let the ConvNeXt-V2 help you uncover it.

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.