segformer_b2_clothes

Maintainer: mattmdjaga

Total Score

225

Last updated 5/17/2024

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PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model Overview

The segformer_b2_clothes model is a Segformer B2 model fine-tuned on the ATR dataset for clothes segmentation by maintainer mattmdjaga. It can also be used for human segmentation. The model was trained on the "mattmdjaga/human_parsing_dataset" dataset.

The Segformer architecture combines a vision transformer with a segmentation head, allowing the model to learn global and local features for effective image segmentation. This fine-tuned version focuses on accurately segmenting clothes and human parts in images.

Model Inputs and Outputs

Inputs

  • Images of people or scenes containing people
  • The model takes the image as input and returns segmentation logits

Outputs

  • Segmentation masks identifying various parts of the human body and clothing
  • The model outputs a tensor of logits, which can be post-processed to obtain the final segmentation map

Capabilities

The segformer_b2_clothes model is capable of accurately segmenting clothes and human body parts in images. It can identify 18 different classes, including hats, hair, sunglasses, upper-clothes, skirts, pants, dresses, shoes, face, legs, arms, bags, and scarves.

The model achieves high performance, with a mean IoU of 0.69 and mean accuracy of 0.80 on the test set. It particularly excels at segmenting background, pants, face, and legs.

What Can I Use it For?

This model can be useful for a variety of applications involving human segmentation and clothing analysis, such as:

  • Fashion and retail applications, to automatically detect and extract clothing items from images
  • Virtual try-on and augmented reality experiences, by accurately segmenting the human body and clothing
  • Semantic understanding of scenes with people, for applications like video surveillance or human-computer interaction
  • Data annotation and dataset creation, by automating the labeling of human body parts and clothing

The maintainer has also provided the training code, which can be fine-tuned further on custom datasets for specialized use cases.

Things to Try

One interesting aspect of this model is its ability to segment a wide range of clothing and body parts. Try experimenting with different types of images, such as full-body shots, close-ups, or images with multiple people, to see how the model performs.

You can also try incorporating the segmentation outputs into downstream applications, such as virtual clothing try-on or fashion recommendation systems. The detailed segmentation masks can provide valuable information about the person's appearance and clothing.

Additionally, the maintainer has mentioned plans to release a colab notebook and a blog post to make the model more user-friendly. Keep an eye out for these resources, as they may provide further insights and guidance on using the segformer_b2_clothes model effectively.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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