NudeNetUpdated 2.2.7

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NudeNet: Neural Nets for Nudity Classification, Detection and selective censoring

Classifier classes:

class name	Description
safe	Image is not sexually explicit
unsafe	Image is sexually explicit

Detector classes:

class name	Description
EXPOSED_ANUS	Exposed Anus; Any gender
EXPOSED_ARMPITS	Exposed Armpits; Any gender
COVERED_BELLY	Provocative, but covered Belly; Any gender
EXPOSED_BELLY	Exposed Belly; Any gender
COVERED_BUTTOCKS	Provocative, but covered Buttocks; Any gender
EXPOSED_BUTTOCKS	Exposed Buttocks; Any gender
FACE_F	Female Face
FACE_M	Male Face
COVERED_FEET	Covered Feet; Any gender
EXPOSED_FEET	Exposed Feet; Any gender
COVERED_BREAST_F	Provocative, but covered Breast; Female
EXPOSED_BREAST_F	Exposed Breast; Female
COVERED_GENITALIA_F	Provocative, but covered Genitalia; Female
EXPOSED_GENITALIA_F	Exposed Genitalia; Female
EXPOSED_BREAST_M	Exposed Breast; Male
EXPOSED_GENITALIA_M	Exposed Genitalia; Male

Usage

As Python module

Installation:

pip install --upgrade nudenetupdated

Classifier Usage:

# Import module
from nudenet import NudeClassifier

# initialize classifier (downloads the checkpoint file automatically the first time)
classifier = NudeClassifier()

# Classify single image
classifier.classify('path_to_image_1')
# Returns {'path_to_image_1': {'safe': PROBABILITY, 'unsafe': PROBABILITY}}
# Classify multiple images (batch prediction)
# batch_size is optional; defaults to 4
classifier.classify(['path_to_image_1', 'path_to_image_2'], batch_size=BATCH_SIZE)
# Returns {'path_to_image_1': {'safe': PROBABILITY, 'unsafe': PROBABILITY},
#          'path_to_image_2': {'safe': PROBABILITY, 'unsafe': PROBABILITY}}

Detector Usage:

# Import module
from nudenet import NudeDetector

# initialize detector (downloads the checkpoint file automatically the first time)
detector = NudeDetector() # detector = NudeDetector('base') for the "base" version of detector.

# Detect single image
detector.detect('path_to_image')
# fast mode is ~3x faster compared to default mode with slightly lower accuracy.
detector.detect('path_to_image', mode='fast')
# Returns [{'box': LIST_OF_COORDINATES, 'score': PROBABILITY, 'label': LABEL}, ...]

Developpers

To get started, simply clone the repository and install the dependencies:

poetry install

Once the dependencies are installed, you can start developing your project.

Command	Description
make test	Run your unit tests
make lint	Lint your code
make format	Format your code
make mypy	Run static type checking

Notes

• Current version of NudeDetector is trained on 160,000 entirely auto-labelled (using classification heat maps and various other hybrid techniques) images.
• The entire data for the classifier is available at https://archive.org/details/NudeNet_classifier_dataset_v1
• A part of the auto-labelled data (Images are from the classifier dataset above) used to train the base Detector is available at https://github.com/notAI-tech/NudeNet/releases/download/v0/DETECTOR_AUTO_GENERATED_DATA.zip

Fork notes

• The original project made by notAI-tech is here
• The forked version made by platelminto taken for this project is here

Contributing

If you have any suggestions for improvements, please feel free to open an issue or submit a pull request.

License

This project is licensed under the MIT License.