With the rapid advancements in artificial intelligence, image classification has become a pivotal task in numerous applications. One of the models making waves in this arena is the FBNet-v3. In this guide, we’ll walk you through setting up and using the FBNet-v3 image classification model using the TIMM library. We’ll cover functionalities for image classification, feature map extraction, and image embeddings.

Getting Started with FBNet-v3

FBNet-v3 is trained on the widely recognized ImageNet-1k dataset and is optimized with advanced recipes to enhance performance. Follow the steps below to get started.

Installation

• Ensure you have Python and necessary packages (like TIMM) installed in your environment.
• If you haven’t already, install TIMM using: pip install timm.

Image Classification with FBNet-v3

Step 1: Load the Model

Start by loading the model with the pretrained weights.

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('fbnetv3_d.ra2_in1k', pretrained=True)
model = model.eval()

Step 2: Prepare the Image

Next, configure the transforms required to preprocess the image.

python
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)

Step 3: Make Predictions

Finally, pass the preprocessed image to the model to get predictions.

python
output = model(transforms(img).unsqueeze(0))  # unsqueeze to create a batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)

Understanding the Code with an Analogy

Think of the entire coding process like preparing a gourmet dish. Here’s how it relates:

• Ingredients: In our case, the raw image is akin to the main ingredient you’d choose for your dish.
• Recipe: The model itself acts like a cooking recipe. Just as recipes evolve, our model has been optimized through advanced techniques to achieve better results.
• Cooking Process: The transformation steps (normalization and resizing) are like prepping your ingredients before cooking—ensuring everything is just right for the best outcome.
• Tasting: Making predictions is like taking the first bite. You get to see if what you’ve prepared meets your expectations!

Feature Map Extraction and Image Embeddings

Besides simply classifying images, FBNet-v3 lets you delve deeper by extracting feature maps and obtaining image embeddings.

Feature Map Extraction

python
model = timm.create_model('fbnetv3_d.ra2_in1k', pretrained=True, features_only=True)
model = model.eval()
output = model(transforms(img).unsqueeze(0)) 
for o in output:
    print(o.shape)  # Display the shape of each feature map

Image Embeddings

python
model = timm.create_model('fbnetv3_d.ra2_in1k', pretrained=True, num_classes=0)  # Remove classifier
model = model.eval()
output = model(transforms(img).unsqueeze(0))  # Output as (batch_size, num_features)

Troubleshooting

If you encounter issues during setup or execution, consider the following troubleshooting tips:

• Double-check your Python environment and library installations.
• Ensure the image URL is accessible and the image is in a supported format.
• Verify that you’re using the correct model name when loading FBNet-v3.

If problems persist, feel free to reach out for support. For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

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.