segformer_b2_clothes

mattmdjaga

Introduction

The SegFormer B2 model is fine-tuned for clothes segmentation using the ATR dataset. It is capable of segmenting various human-related features and can be applied to human segmentation tasks. The model is part of the Hugging Face ecosystem and is compatible with PyTorch and ONNX.

Architecture

The model uses a transformer-based architecture known as SegFormer, which is designed for efficient and effective semantic segmentation. It leverages a simple yet powerful design to achieve high accuracy in image segmentation tasks.

Training

The model is fine-tuned on the ATR dataset, which is part of the "mattmdjaga/human_parsing_dataset" available on Hugging Face. The training process involves using a SegformerImageProcessor and a pre-trained AutoModelForSemanticSegmentation. It outputs logits, which are then interpolated to match the input image size and are used to generate the segmentation predictions.

Guide: Running Locally

  1. Install Dependencies: Ensure that transformers, torch, PIL, and matplotlib are installed in your Python environment.
  2. Load Model: Use the SegformerImageProcessor and AutoModelForSemanticSegmentation from the transformers library.
  3. Process Image: Download an image and process it using the image processor to get tensors.
  4. Get Predictions: Run the model to get logits, interpolate them, and visualize the segmentation map using matplotlib.

For optimal performance, it is recommended to run this model on a machine with a GPU. Cloud services such as AWS, Google Cloud, or Azure offer GPU instances that can handle such tasks efficiently.

License

The model is distributed under the MIT License. For more details, refer to the license document.

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