The EfficientNet-v2 model is a state-of-the-art image classification tool, trained on the ImageNet-1k dataset. It’s created using TensorFlow and expertly ported to PyTorch by Ross Wightman. In this blog, we will guide you through the process of setting up and using this incredible model effectively.

Model Overview

Before diving into the practical steps, here’s a brief overview of the model:

• Model Type: Image classification feature backbone
• Parameters: 118.5 million
• GMACs: 36.1
• Activations: 101.2 million
• Image Size: Train – 384×384, Test – 480×480
• Papers: EfficientNetV2: Smaller Models and Faster Training
• Dataset: ImageNet-1k
• Original Repository: EfficientNet GitHub Repository

Using the Model

Here’s how you can use the EfficientNet-v2 model for image classification, feature map extraction, and image embeddings quickly and efficiently:

Image Classification

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/beignet-task-guide.png))
model = timm.create_model('tf_efficientnetv2_l.in1k', pretrained=True)
model = model.eval()

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)

Feature Map Extraction

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/beignet-task-guide.png))
model = timm.create_model('tf_efficientnetv2_l.in1k', pretrained=True, features_only=True)
model = model.eval()

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)

Image Embeddings

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/beignet-task-guide.png))
model = timm.create_model('tf_efficientnetv2_l.in1k', pretrained=True, num_classes=0)  # remove classifier nn.Linear
model = model.eval()

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
output = model.forward_features(transforms(img).unsqueeze(0))  # output is unpooled, a (1, 1280, 12, 12) shaped tensor
output = model.forward_head(output, pre_logits=True)  # output is a (1, num_features) shaped tensor

Analogy: Building with Blocks

Think of using the EfficientNet-v2 model as building a structure with blocks:

• **Image Classification**: This step is like placing the foundation blocks together, allowing you to see what your structure might look like.
• **Feature Map Extraction**: Here, you’re examining each level of your structure layer by layer, understanding how each block supports the next.
• **Image Embeddings**: Lastly, this is akin to creating a 2D representation of your structure. You can still see the essence of what you’ve built without needing all the details.

Troubleshooting

If you run into issues while working with the EfficientNet-v2 model, consider the following troubleshooting ideas:

• Ensure you have all the necessary libraries installed (like timm and PIL). A simple way to install them is using pip install timm Pillow.
• Check the compatibility of the image URLs you are using. Make sure they are accessible.
• If the image output appears incorrect, ensure your image preprocessing (like resizing and normalization) matches the model’s requirements dictated by timm.data.resolve_model_data_config(model).
• For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

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.

Model Comparison

To further explore the dataset and runtime metrics of this model, check out the model results on GitHub.