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

Uncensored version of the following image can be found at https://i.imgur.com/rga6845.jpg (NSFW)

Classifier classes:

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

Default 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

Base Detector classes:

class name	Description
EXPOSED_BELLY	Exposed Belly; Any gender
EXPOSED_BUTTOCKS	Exposed Buttocks; Any gender
EXPOSED_BREAST_F	Exposed Breast; Female
EXPOSED_GENITALIA_F	Exposed Genitalia; Female
EXPOSED_GENITALIA_M	Exposed Genitalia; Male
EXPOSED_BREAST_M	Exposed Breast; Male

As self-hostable API service

# Classifier
docker run -it -p8080:8080 notaitech/nudenet:classifier

# Detector
docker run -it -p8080:8080 notaitech/nudenet:detector

# See fastDeploy-file_client.py for running predictions via fastDeploy's REST endpoints 
wget https://raw.githubusercontent.com/notAI-tech/fastDeploy/master/cli/fastDeploy-file_client.py
# Single input
python fastDeploy-file_client.py --file PATH_TO_YOUR_IMAGE

# Client side batching
python fastDeploy-file_client.py --dir PATH_TO_FOLDER --ext jpg

Note: golang example #63 (comment), thanks to Preetham Kamidi

As Python module

Installation:

pip install --upgrade nudenet

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}}

# Classify video
# batch_size is optional; defaults to 4
classifier.classify_video('path_to_video', batch_size=BATCH_SIZE)
# Returns {"metadata": {"fps": FPS, "video_length": TOTAL_N_FRAMES, "video_path": 'path_to_video'},
#          "preds": {frame_i: {'safe': PROBABILITY, 'unsafe': PROBABILITY}, ....}}

Thanks to Johnny Urosevic, NudeClassifier is also available in tflite.

TFLite Classifier Usage:

# Import module
from nudenet import NudeClassifierLite

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

# Classify single image
classifier_lite.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_lite.classify(['path_to_image_1', 'path_to_image_2'])
# Returns {'path_to_image_1': {'safe': PROBABILITY, 'unsafe': PROBABILITY},
#          'path_to_image_2': {'safe': PROBABILITY, 'unsafe': PROBABILITY}}

Using the tflite classifier from flutter: https://github.com/ndaysinaiK/nude-test

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}, ...]

# Detect video
# batch_size is optional; defaults to 2
# show_progress is optional; defaults to True
detector.detect_video('path_to_video', batch_size=BATCH_SIZE, show_progress=BOOLEAN)
# fast mode is ~3x faster compared to default mode with slightly lower accuracy.
detector.detect_video('path_to_video', batch_size=BATCH_SIZE, show_progress=BOOLEAN, mode='fast')
# Returns {"metadata": {"fps": FPS, "video_length": TOTAL_N_FRAMES, "video_path": 'path_to_video'},
#          "preds": {frame_i: {'box': LIST_OF_COORDINATES, 'score': PROBABILITY, 'label': LABEL}, ...], ....}}

Notes:

detect_video and classify_video first identify the "unique" frames in a video and run predictions on them for significant performance improvement.
V1 of NudeDetector (available in master branch of this repo) was trained on 12000 images labelled by the good folks at cti-community.
V2 (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

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Repository Details

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

As self-hostable API service

As Python module

Notes:

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