EfficientNet-v2 is a high-performance image classification model designed to provide state-of-the-art results while maintaining a small footprint. In this guide, we will walk you through how to use the EfficientNet-v2 model implemented in the timm library, making it easier than ever to classify images with precision!

Model Overview

The EfficientNet-v2 model has been trained on the popular ImageNet-1k dataset. Here are some key details:

• Model Type: Image classification feature backbone
• Parameters: 21.5M
• GMACs: 5.4
• Activations: 22.7M
• Training Image Size: 300 x 300
• Testing Image Size: 384 x 384

For further reading, check the original paper on EfficientNetV2: Smaller Models and Faster Training.

How to Use EfficientNet-v2 for Image Classification

Follow these steps to get started with image classification using the EfficientNet-v2 model:

1. Set Up Your Environment

Before diving into coding, ensure you have the necessary libraries installed. You will need Python, the timm library, and Pillow for image processing.

2. Load the Model

Here’s how to load the EfficientNet-v2 model and make predictions on an image:

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

# Load an image from a URL
img = Image.open(urlopen('https://huggingface.co/datasets/huggingfaced/documentation-images/resolve/main/beignets-task-guide.png'))

# Create and eval the model
model = timm.create_model('tf_efficientnetv2_s.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 and get output
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)

3. Analyzing the Code

Think of the above code as preparing a delicious recipe.

• First, you gather your ingredients (load an image).
• Next, you preheat the oven (create the model).
• Then, you mix your ingredients (transform the image).
• Finally, you place your dish in the oven (model prediction) and wait for the delicious result (get probabilities and class indices).

Feature Map Extraction

To extract feature maps that represent different layers of information in the image, use the following code:

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

# Create model for feature extraction
model = timm.create_model('tf_efficientnetv2_s.in1k', pretrained=True, features_only=True)
model = model.eval()

# Get model specific transforms
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)

# Process the image and extract features
output = model(transforms(img).unsqueeze(0))  # unsqueeze single image into a batch of 1

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

Image Embeddings

If you need to generate image embeddings for downstream tasks, this code snippet will help you do that:

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

# Create model for embeddings
model = timm.create_model('tf_efficientnetv2_s.in1k', pretrained=True, num_classes=0)  # Remove classifier layer
model = model.eval()

# Get model specific transforms
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)

# Generate embeddings
output = model(transforms(img).unsqueeze(0))  # Output is (batch_size, num_features) shaped tensor
output = model.forward_features(transforms(img).unsqueeze(0))  # (1, 1280, 10, 10) shaped tensor
output = model.forward_head(output, pre_logits=True)  # output is a (1, num_features) shaped tensor

Troubleshooting

During the implementation of EfficientNet-v2, you might face some challenges. Here are some tips:

• Model Loading Issues: Ensure you have installed the latest version of the timm library and PyTorch.
• Image Processing Errors: Check that the image URL is valid and accessible. Using local images might be beneficial for testing.
• Shape Mismatches: Verify the input shapes and the model expectations, particularly if modifying layers.

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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.