segformer_b3_clothes

Introduction

SegFormer B3 is a model fine-tuned for clothes segmentation, capable of segmenting various human clothing items. It utilizes the ATR dataset and can also be used for general human segmentation tasks. The model is trained and available on Hugging Face under the repository sayeed99/segformer_b3_clothes.

Architecture

This implementation leverages the SegFormer architecture, known for its simple and efficient design suitable for semantic segmentation tasks. The model uses transformers to achieve state-of-the-art segmentation performance.

Training

The model is fine-tuned on the mattmdjaga/human_parsing_dataset, a comprehensive dataset for human clothes segmentation. The training code is available on GitHub, and further resources like a Colab notebook and a blog post are expected to be released to assist users in understanding and replicating the training process.

Guide: Running Locally

1. Install Required Libraries: Ensure you have Python installed along with libraries such as transformers, PIL, requests, matplotlib, and torch.

2. Load the Model:

   from transformers import SegformerImageProcessor, AutoModelForSemanticSegmentation
   from PIL import Image
   import requests
   import matplotlib.pyplot as plt
   import torch.nn as nn
   
   processor = SegformerImageProcessor.from_pretrained("sayeed99/segformer_b3_clothes")
   model = AutoModelForSemanticSegmentation.from_pretrained("sayeed99/segformer_b3_clothes")
   

3. Process an Image:

   url = "IMAGE_URL_HERE"
   image = Image.open(requests.get(url, stream=True).raw)
   inputs = processor(images=image, return_tensors="pt")
   outputs = model(**inputs)
   logits = outputs.logits.cpu()
   

4. Visualize Segmentation:

   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)
   

5. Consider Using Cloud GPUs: For efficient processing and training, consider using cloud-based GPUs such as those provided by AWS, GCP, or Azure.

License

The SegFormer B3 model is released under the MIT License. The full license details can be found in the SegFormer GitHub repository.