The ConvNeXt image classification model is a powerful tool for processing images, particularly those used in applications such as computer vision. Pretrained on a vast dataset (ImageNet-22k) and fine-tuned on a smaller but relevant dataset (ImageNet-1k), this model allows you to leap straight into sophisticated image classification tasks without the need for extensive training efforts.

Model Details

• **Model Type:** Image Classification Feature Backbone
• **Parameters:** 28.6M
• **GMACs:** 13.1
• **Activations:** 39.5M
• **Image Size:** 384 x 384
• **Pretrained Dataset:** ImageNet-22k
• **Finetuned Dataset:** ImageNet-1k

Getting Started with Image Classification

To start utilizing the ConvNeXt model for image classification, you can follow these straightforward steps:

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

img = Image.open(urlopen("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png"))
model = timm.create_model("convnext_tiny.fb_in22k_ft_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 a batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)

In the above analogy, think of the image classifier as a skilled chef. The image is your dish, which you want to classify as a spaghetti or a pizza. By using ConvNeXt, it’s like you’ve invited a master chef who has an extensive recipe book (the pre-trained dataset). The chef knows which ingredients go into different dishes and can instantly tell you if your dish is pasta or pizza by analyzing its appearance.

Feature Map Extraction

In case you want to analyze the underlying features that the model uses to classify images, you can extract feature maps:

python
model = timm.create_model("convnext_tiny.fb_in22k_ft_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))  

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

Creating Image Embeddings

To create embeddings from an image (an abstract representation), you may want to use the following code:

python
model = timm.create_model("convnext_tiny.fb_in22k_ft_in1k_384", pretrained=True, num_classes=0)  # Remove classifier nn.Linear
model = model.eval()

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

Continually think of our chef analogy here; the feature maps represent various techniques such as chopping or mixing. Each of these processes defines different aspects of the dish — just as the feature maps define different aspects of the image classification task.

Model Comparison

For deeper insights, you can explore model results and compare metrics [here](https://github.com/huggingface/pytorch-image-models/tree/main/results) to select the best model suited for your specific need.

Troubleshooting Instructions

If you encounter any issues while using the ConvNeXt model, please consider the following troubleshooting ideas:

• Ensure that your Python environment has all necessary libraries installed, including `timm`, `torch`, and `PIL`.
• Check if the image URL is correct and accessible.
• Verify that the image format is supported by PIL.
• If the model does not return any predictions, look into the transform configurations to ensure they match the model’s expected input shape.

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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.