Mattmdjaga

segformer_b2_clothes

mattmdjaga

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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.

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