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human-body-segmentation

Image Segmentation
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tensorflow
human-body
deeplabv3+
resnet50
inpainting
generative-ai
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metadata
language: en
tags:
  - image-segmentation
  - human-body
  - deeplabv3+
  - resnet50
  - inpainting
  - generative-ai
license: mit
datasets:
  - fashionpedia
metrics:
  - mean_iou
model_name: Human Body Segmentation Model
model_type: deeplabv3+
library_name: tensorflow

Model Card: Human Body Segmentation Model

Model Details

  • Model Name: Human Body Segmentation Model
  • Version: 1.0
  • Architecture: DeeplabV3Plus with ResNet50 backbone
  • License: MIT

Intended Use

  • Primary Use Cases: This model is designed to segment parts of the human body in images. It is primarily intended for automating the masking process for inpainting features of generative AI tools like Stable Diffusion. This allows users to select certain parts of the body and use prompts to change clothes or other features through generative AI.
  • Primary Users: Researchers, developers, and businesses working in the fields of computer vision, image editing, and generative AI.
  • Out-of-Scope Use Cases: This model is not intended for applications where precise segmentation is critical for safety, such as medical diagnosis or autonomous driving.

Performance

  • Benchmark Results: The model achieves [insert metric, e.g., 85% mIoU] on the [specify dataset, e.g., Fashionpedia dataset].
  • Evaluation Process: The model was evaluated using standard image segmentation benchmarks with metrics such as mean Intersection over Union (mIoU).
  • Comparison: Compared to other segmentation models, this model performs [mention if better/worse] in terms of [specific metrics].

Training Data

  • Dataset: The model was trained on the Fashionpedia dataset.
  • Preprocessing: The images were resized to 256x256 pixels and normalized. Data augmentation techniques such as flipping, rotation, and scaling were applied to increase the dataset's variability.

Limitations

  • Known Limitations: The model may not perform well on images with significantly different characteristics from the training dataset. It may also struggle with very small or very large objects in images.
  • Potential Biases: The model could inherit biases present in the training data, such as underrepresentation of certain classes.

Ethical Considerations

  • Ethical Issues: The use of this model in applications where incorrect segmentation could lead to harm (e.g., medical diagnosis, autonomous driving) should be carefully evaluated.
  • Mitigation Strategies: Users should validate the model's performance on their specific data and consider using additional validation steps to ensure safety and reliability.

Installation

To install the necessary dependencies, run:

pip install -r requirements.txt

Example Usage 

import tensorflow as tf
from tensorflow.keras.preprocessing import image
import numpy as np

# Load the model
model = tf.keras.models.load_model('path/to/model')

# Load and preprocess the image
img_path = 'path/to/image.jpg'
img = image.load_img(img_path, target_size=(256, 256))
img_array = image.img_to_array(img)
img_array = np.expand_dims(img_array, axis=0)
img_array /= 255.0

# Predict the mask
predicted_mask = model.predict(img_array)

# Display the mask
import matplotlib.pyplot as plt
plt.imshow(predicted_mask[0, :, :, 0], cmap='gray')
plt.show()

Thank you for considering this model for your image segmentation needs. We look forward to your feedback and contributions!