Image segmentation is a critical technique in computer vision, allowing us to classify pixels in an image into distinct categories. In this blog post, we’ll walk through how to use the SegFormer-B2-Fashion model for semantic segmentation of images, specifically focusing on fashion items.

Getting Started

Before diving into the code, ensure you have the necessary libraries installed:

• PyTorch
• Transformers
• Matplotlib

Install these libraries using:

pip install torch torchvision transformers matplotlib

Code Walkthrough

Here’s the code snippet to set up our image segmentation model:

from transformers import SegformerImageProcessor, AutoModelForSemanticSegmentation
from PIL import Image
import requests
import matplotlib.pyplot as plt
import torch.nn as nn

# Load the image processor and model
processor = SegformerImageProcessor.from_pretrained("sayeed99/segformer-b2-fashion")
model = AutoModelForSemanticSegmentation.from_pretrained("sayeed99/segformer-b2-fashion")

# Load an image
url = "https://plus.unsplash.com/premium_photo-1673210886161-bfcc40f54d1f?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxzZWFyY2h8MXx8cGVyc29uJTIwc3RhbmRpbmd8ZW58MHx8MHx8&w=1000&q=80"
image = Image.open(requests.get(url, stream=True).raw)

# Prepare the image for the model
inputs = processor(images=image, return_tensors="pt")

# Get model predictions
outputs = model(**inputs)
logits = outputs.logits.cpu()

# Upsample the logits for visualization
upsampled_logits = nn.functional.interpolate(
    logits,
    size=image.size[::-1],
    mode='bilinear',
    align_corners=False,
)

# Use argmax to get prediction
pred_seg = upsampled_logits.argmax(dim=1)[0]

# Display the segmented image
plt.imshow(pred_seg)
plt.show()

Understanding the Code with an Analogy

Think of the segmentation process as a chef in a kitchen, carefully selecting which ingredients (pixels) go into each dish (image category). The chef starts by gathering a variety of ingredients (the image data), and then processes them (using the image processor) to ensure they are ready for cooking (model prediction). The chef then places the prepared ingredients into each dish based on a recipe (the segmentation labels). Finally, the chef presents the beautifully plated dishes for everyone to see (visualization of segmented images).

Segmentation Labels

The model segments various articles of clothing into specific categories. Here’s a glimpse of the labels used:

• 0: Everything Else
• 1: Shirt, Blouse
• 2: Top, T-shirt, Sweatshirt
• 3: Sweater
• 4: Cardigan
• 5: Jacket
• … (and many more)

Troubleshooting Tips

If you encounter issues while implementing the model, consider the following:

• Model Not Found: Ensure you’re using the correct model name when loading the SegFormer.
• Image Loading Issues: Check the URL for accessibility. If the image URL fails, try another valid image link.
• Library Compatibility: Make sure your installed library versions align with the framework requirements mentioned.
• GPU Usage: If your model runs slowly, consider using a GPU instead of a CPU for processing.

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

Conclusion

Image segmentation is a powerful tool that can enhance the way we analyze visual data. By leveraging the SegFormer-B2-Fashion model, you can efficiently categorize and visualize various fashion items within images. 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.