EfficientNet is a powerful model for image classification, making it easier than ever to classify images efficiently and accurately. In this guide, we will explore how to use the EfficientNet image classification model trained on ImageNet-1k with the TIMM library.

Model Overview

The tf_efficientnet_b7.ap_in1k is a state-of-the-art model specifically designed for image classification. It has been meticulously developed and validated with a range of parameters:

• Params (M): 66.3
• GMACs: 38.3
• Activations (M): 289.9
• Image Size: 600 x 600

This model is expected to produce excellent results thanks to its impressive architecture and the training dataset used.

Model Usage

To get started with image classification using EfficientNet, follow these steps:

Step 1: Load the Necessary Libraries

You’ll need to import several libraries including TIMM, PIL, and URL handling libraries.

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

Step 2: Open an Image

Load your image from a URL. In this example, we will use an image hosted online.

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

Step 3: Create the Model Instance

Next, you will create an instance of the EfficientNet model with pretrained weights.

model = timm.create_model("tf_efficientnet_b7.ap_in1k", pretrained=True)
model = model.eval()

Step 4: Prepare the Input Data

Transform the image to fit the model’s requirements:

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))

Step 5: Get Prediction Results

You can now obtain the top 5 probabilities and class indices of the image.

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

Feature Map Extraction

If you’re interested in extracting feature maps from the model at different layers, you can modify the model initialization:

model = timm.create_model("tf_efficientnet_b7.ap_in1k", pretrained=True, features_only=True)
model = model.eval()

Now you can access the output feature maps:

output = model(transforms(img).unsqueeze(0))
for o in output:
    print(o.shape)

Image Embeddings

For image embeddings, configure the model to exclude the classifier:

model = timm.create_model("tf_efficientnet_b7.ap_in1k", pretrained=True, num_classes=0)
model = model.eval()

You can obtain the embeddings as follows:

output = model(transforms(img).unsqueeze(0))

Troubleshooting

If you run into issues while implementing the model, here are a few tips:

• Image not loading: Ensure the image URL is correct and accessible.
• Model not found: Double-check that the model name is entered correctly when creating the model instance.
• Unexpected Tensor shapes: Verify your input image transformations are compatible with the model’s expectations.
• Installation Problems: Ensure that the TIMM library and its dependencies are correctly installed.

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Conclusion

Using EfficientNet for image classification can significantly enhance your image processing projects. With step-by-step instructions provided in this guide, you should be well-equipped to harness its potential.

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.