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 |
# 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 notAI-tech#63 (comment), thanks to Preetham Kamidi
Installation:
# tensorflow/ tensorflow-gpu <= 1.15.4 is required
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}, ...], ....}}
- 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