Master Segformer

Introduction

A model that segments clothes and humans into different labels would have many applications today. This model’s ability is based on image processing and fine-tuning efficiency. Image processing is done in different ways, and that is where image segmentation comes into the illustration.

This process involves grouping each pixel in an image and identifying them using a label (The label usually comes out in the output as different colors). It is a computer vision technology that detects regions in an image after processing. So, it can identify objects, including backgrounds, hands, heads, and vehicles. However, what this model can detect deeply varies with its training and fine-tuning. 

Many image segmentation technologies have been developed for different use cases. They can detect the body, clothes, and other image areas.

Learning Objectives

• Understanding Segformer’s fine-tuning and capabilities.
• Gain insight into the types and use cases of Segformer B2_Clothes.
• Running Inference and with Segformer.
• Learn real-life applications of Segformer.

This article was published as a part of the Data Science Blogathon.

What is Segformer?

The primary function of Segformer and similar tools is to break a digital image into multiple segments. They represent the image meaningfully and make every region easy to analyze. This means all the pixels in the same category have a standard label assigned to them. 

The terms ‘image processing’ and ‘image segmentation’ are different. Image processing refers to changing an image into a digital form and performing operations to extract valuable data. Relatively, segmentation is a type of image processing that can differ by its capabilities or training to identify different elements or objects within an image. 

Image segmentation can be divided into different categories depending on the task it can perform and its other capabilities. A good example is region-based segmentation; it is suitable for segmenting areas of any image that share similarities in color, texture, and intensity. This approach has many applications in healthcare, including MRI and CT scans. 

Another type is edge segmentation, which works to identify the boundaries within an image. This is why it is essential for self-driving cars. Clustering-based, instance and thresholding segmentation are other image segmentation categories.

What is the Architecture of Segformer? 

Segformer uses a transformer-based model, which means there is an encoder-decoder endpoint in the process. The encoder is the transformer model, while the decoder is an MLP decoder; this architecture differs from the traditional computer vision and language processing other models employ.  

These two parts of the image processing procedure have various components. The transformer encoder comprises multi-head attention, feedforward, and patch merging components. On the other hand, the decoder includes linear and Upsampling layers. 

The Transformer encoder divides each image into patches. The patch merging layers pool features from these patches in an overlapping fashion. This model’s patch-merging process helps preserve local features and continuity, enhancing performance. 

The basis of this model’s architecture lies within three key points: It does not use positional encoding to ensure the design has simple and efficient semantic segmentation. Another strong framework in this model is its efficient self-attention mechanism. The reason for this mechanism is to reduce computational requirements, so a vision transformer plays a massive role. 

Finally, the MLP decoder has a multiscaling feature that helps with computation. A full MLP has broader receptive fields, making segmentation better than other decoders. 

Segformer Vs. Others: How Does this Model Stand Out?

Segformer is just one among many other image segmentation models. It has a few advantages over other transformer-based segmentation models. This model is trained with an ImageNet architecture, which reduces its computational requirement. Segformer also has attributes in its architecture that ensure it can learn coarse and fine features in an image’s pixel. 

Positional encoding is one feature that can slow down this model’s inference time. Segfomer’s lack of this feature means it can have a faster run time than other transformer-based models. 

Training Segformer

This model can be trained from scratch or through a hugging face library. Both methods are efficient, but hugging face simplifies the whole process. If you want to train this data from scratch, it involves a few steps towards getting the results. 

Training this model from scratch would start with data processing, which involves loading the images and labels from the files. Another step is testing the difference between the model’s prediction of the label and the label itself. All this would be done before you can assess the performance. 

On the other hand, Hugging face streamlines the whole process. First, you use an API to prepare the data before fine-tuning and evaluation. 

However, training this model from scratch would give you good customization and control. However, hugging face pre-trained data can offer a strong framework while limiting your control over customization. 

Advantages of Segformer Model

Many features make this model more beneficial to others of its kind. These are a few advantages of Segformer: 

• Its straightforward architecture that does not need complicated training designs can be a huge advantage.
• Segformer is versatile enough to deliver various domain-specific tasks with the right fine-tuning.
• Many other transformer-based models can only work with a specific image resolution. Segformer overcomes this obstacle by being efficient with any image size or format.

Possible Limitations 

The quality of training data plays a significant part in the image segmentation process. If you have limited data, the model may perform within the range of images you use. The best way to solve this problem is to provide enough diversity in the training data and ensure you use images with various scenarios, diversity, and lighting. 

Another factor that can affect the performance of this model is the choice of algorithms and tuning. You must select the right algorithm and optimize its parameters for every task.

Integrating Segformer and many other image segmentation models can be challenging. This problem is due to the various data formats the system has to handle. However, using APIs and well-designed interfaces can help curb this problem. 

Complex object shapes and sizes can dent the accuracy and precision of this model. But that is where the evaluation metrics come in handy. You can test segmentation models with metrics like pixel accuracy and dice coefficient. Model refinement through iterative training and fine-tuning is also another effective way to improve the performance of these types of models. 

How to Use Segformer B2 Clothes?

We will run inference with this Segfomer model, fine-tuned for clothes segmentation. It can also be used for human segmentation so that the labels can categorize body parts. 

This model has been trained on the ATR Data set, giving you these capabilities. 

First, you have to install the necessary libraries in the Python environment.  

!pip install transformers pillow matplotlib torch

Step1: Importing Necessary Libraries

This step imports the necessary modules for using Segformer in the Python environment.  The Segformer model will take an image, preprocess it with the SegformerImageProcessor, and perform segmentation. The results can also be seen with the ‘matplotlib’. 

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

Step2: Initializing the Segformer by Loading Pre-trained Data

You must load the pre-trained image processor to start the image processing step. These lines of code initialize image processing and load the model for segmentation tasks. 

processor = SegformerImageProcessor.from_pretrained("mattmdjaga/segformer_b2_clothes")
model = AutoModelForSemanticSegmentation.from_pretrained("mattmdjaga/segformer_b2_clothes")

Step3: Image Processing 

This is where we bring in the image URL we want to segment. We then use the tensor to process the image and provide the required output, delivering human and clothes segmentation. 

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)
inputs = processor(images=image, return_tensors="pt")

Step4: Running Segformer Model on Processed Image

The final steps of this image processing involve running the Segformer model on the processed image inputs and generating segmentation logits. Thus, image processing ensures that segmentation occurs with every image pixel.

Here is what the code looks like:

outputs = model(**inputs)
logits = outputs.logits.cpu()

upsampled_logits = nn.functional.interpolate(
   logits,
   size=image.size[::-1],
   mode="bilinear",
   align_corners=False,
)

pred_seg = upsampled_logits.argmax(dim=1)[0]
plt.imshow(pred_seg)

Output:

The image before and after the segmentation shows how this code generated an output that identifies the human and cloth elements. When using API the labels identify every element and the colors that represents each of them. 

Note: If you run into an error or any other issue while running this model, there are a few troubleshooting tips you should know. Always ensure that all the libraries you import are updated and compatible with your Python version. Confirming the image size and format when working on the input is important, as you may encounter an input or output error. 

Performance Benchmark of the Segformer Model

Segformer models have been tested to show superior performance across benchmarks compared to alternatives such as ADE20K and cityscapes. This adds to the fact that the model is robust and semantic segmentation. 

Real-Life Application of Segformer Models

Image processing and segmentation have found their application in different fields today. This model has a long list of use cases, and we will highlight a few of them. They include: 

• Medical Scans: This model helps medical imaging detect tumors and other disease diagnoses. During MRI and CT scans, it can group organs from other irregularities in the body. 
• Autonomous Vehicles: Another new technology that finds image processing with Segformer and similar models useful in the self-driving driving industry. This tool allows the self-driving vehicle to detect cars, roads, and other obstacles to avoid accidents.
• Remote Sensing: Satellite image analysis is another big part of segmentation. It is especially useful for monitoring changes in a landscape over time and natural resources. 
• Document Scanning and OCR: Image segmentation can be valuable in scanning documents and OCR systems. OCR systems recognize text from images, and image segmentation helps to extract text from multiple scanned documents automatically. 
• Retailers and E-Commerce Businesses: These businesses can use image segmentation to identify and group items. This can help reduce complications in inventory tracking and increase the time needed to identify products.

Conclusion

Image processing and segmentation attain a new benchmark with Segformer. A transformer-based architecture is a game-changer that helps the model stand out with unique attributes like faster inference time and low computational requirements. However, Segfomer still has a vast range of abilities and applications; that is where the pretraining masterclass comes into the picture. 

Accuracy and precision are important parts of this model, and its performance significantly depends on the efficiency of the training data. 

Key Takeaways

• Segformer’s versatility makes it outstanding. This tool takes a flexible approach to image segmentation, allowing users to perform various tasks with the right pre-training and fine-tuning. 
• Using transformer-based architecture and MiT backbone guarantees the model’s accuracy when handling various tasks. It also contributes to low computational requirements and faster inference time. 
• The steps to running inference with Segformer are also simple. Everything from loading pre-trained data to image processing and visualizing segmentation is straightforward. 
• Improving the diversity and quality of the training data is the key to better precision and accuracy with this model.

Research Resources

Frequently Asked Questions

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Hey there! I’m David Maigari a dynamic professional with a passion for technical writing writing, Web Development, and the AI world. David is an also enthusiast of data science and AI innovations.