Welcome to the world of image classification! In this article, we will guide you through the implementation of the FBNet image classification model using the Timm library. Equipped with the understanding of its architecture and functionality, you’ll be able to harness its power for your own projects. Let’s dive in!

What is FBNet?

FBNet is an efficient convolutional neural network designed to perform image classification tasks. Trained on the ImageNet-1k dataset, it utilizes advanced techniques like differentiable neural architecture search to create a model that is both lightweight and highly performant.

Getting Started: Setting Up Your Environment

Before we begin, make sure you have the following Python packages installed:

• timm – The Timm library for image models.
• PIL – Python Imaging Library for opening and manipulating images.
• torch – PyTorch for tensor computations.

Implementing Image Classification

Now, let’s break down the recipe for image classification using FBNet. Imagine this like preparing a dish from a recipe: you need the right ingredients, a step-by-step approach, and a taste of checking the results at every stage. Below is the code that will help you classify images using the FBNet model.

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

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

# Create and load the model
model = timm.create_model("fbnetc_100.rmsp_in1k", pretrained=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)

# Classify the image
output = model(transforms(img).unsqueeze(0))

# Get the top 5 class probabilities
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)

Understanding the Code

Think of the code you just read as a recipe for baking a cake. Each part represents a specific step to achieving the final result, which is the classified output. Here’s a breakdown:

• Loading the Image: Just as you need ingredients, we start by loading our image to classify.
• Creating the Model: We prepare our baking dish (the model) with pre-trained weights.
• Transforming the Image: Like prepping ingredients, we apply necessary transformations to the image before feeding it into the model.
• Classifying: After the model processes the transformed image, it predicts the probabilities of the top 5 classes—just like checking if your cake is done!

Extracting Feature Maps

In addition to basic classification, we can extract feature maps for advanced insights. This is akin to examining the individual layers of a cake to appreciate your baking skills better. Here’s how you can get the feature maps:

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

# Create and load the model for feature extraction
model = timm.create_model("fbnetc_100.rmsp_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)

# Extract feature maps
output = model(transforms(img).unsqueeze(0))

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

Creating Image Embeddings

Want to create embeddings from your images? It’s like making a delicious frosting to embellish your cake. Here’s how you can produce embeddings:

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

# Create the model for embeddings
model = timm.create_model("fbnetc_100.rmsp_in1k", pretrained=True, num_classes=0)
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)

# Get image embeddings
output = model.forward_features(transforms(img).unsqueeze(0))
# Output is a (1, 1984, 7, 7) shaped tensor
output = model.forward_head(output, pre_logits=True)
# Output is a (1, num_features) shaped tensor

Troubleshooting

If you encounter issues while running the code, consider the following troubleshooting tips:

• Ensure all required libraries are properly installed and updated.
• Double-check the URL to the image; ensure it is accessible.
• Verify that your environment supports the timm library, and that it’s compatible with your version of Python.
• If you’re getting errors related to transformations, revisit the model’s data config to ensure you are applying the correct parameters.

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

Conclusion

With these steps, you can unleash the power of FBNet for your image classification tasks. 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.

Further Exploration

Feel free to compare your model’s performance and explore various metrics on the GitHub repository.