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Project description
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.
Project details
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