imgutils.validate.teen

Overview:

A model for classifying teen anime images into 4 classes (contentious, safe_teen, non_teen").

The following are sample images for testing.

../../_images/teen.plot.py.svg

This is an overall benchmark of all the classification validation models:

../../_images/teen_benchmark.plot.py.svg

The models are hosted on huggingface - deepghs/anime_teen.

anime_teen_score

imgutils.validate.teen.anime_teen_score(image: str | PathLike | bytes | bytearray | BinaryIO | Image, model_name: str = 'mobilenetv3_v0_dist') → Dict[str, float][source]
Overview:

Predict the class of the given image, return the score with as a dict object.

Parameters:

  • image – Image to teen.

  • model_name – Model to use. Default is mobilenetv3_v0_dist. All available models are listed on the benchmark plot above. If you need better accuracy, just set this to caformer_s36_v0.

Returns:

A dict with classes and scores.

Examples::
>>> from imgutils.validate import anime_teen_score
>>>
>>> anime_teen_score('teen/contentious/1.jpg')
{'contentious': 0.9998493194580078, 'safe_teen': 3.0378791052498855e-05, 'non_teen': 0.00012023092131130397}
>>> anime_teen_score('teen/contentious/2.jpg')
{'contentious': 0.9790042638778687, 'safe_teen': 0.0017522255657240748, 'non_teen': 0.01924353837966919}
>>> anime_teen_score('teen/contentious/3.jpg')
{'contentious': 0.9998124241828918, 'safe_teen': 4.19778298237361e-05, 'non_teen': 0.0001456339523429051}
>>> anime_teen_score('teen/safe_teen/4.jpg')
{'contentious': 0.0008521362324245274, 'safe_teen': 0.9989691972732544, 'non_teen': 0.00017870066221803427}
>>> anime_teen_score('teen/safe_teen/5.jpg')
{'contentious': 6.0992944781901315e-05, 'safe_teen': 0.9994398951530457, 'non_teen': 0.0004991036257706583}
>>> anime_teen_score('teen/safe_teen/6.jpg')
{'contentious': 5.2035720727872103e-05, 'safe_teen': 0.9994019269943237, 'non_teen': 0.0005460577667690814}
>>> anime_teen_score('teen/non_teen/7.jpg')
{'contentious': 3.0478151529678144e-05, 'safe_teen': 3.524079147609882e-05, 'non_teen': 0.999934196472168}
>>> anime_teen_score('teen/non_teen/8.jpg')
{'contentious': 9.786742884898558e-05, 'safe_teen': 8.653994154883549e-05, 'non_teen': 0.9998156428337097}
>>> anime_teen_score('teen/non_teen/9.jpg')
{'contentious': 0.0001218809193233028, 'safe_teen': 0.00013706681784242392, 'non_teen': 0.9997410178184509}

anime_teen

imgutils.validate.teen.anime_teen(image: str | PathLike | bytes | bytearray | BinaryIO | Image, model_name: str = 'mobilenetv3_v0_dist') → Tuple[str, float][source]
Overview:

Predict the class of the given image, return the class and its score.

Parameters:

  • image – Image to teen.

  • model_name – Model to use. Default is mobilenetv3_sce_dist. All available models are listed on the benchmark plot above. If you need better accuracy, just set this to caformer_s36_plus.

Returns:

A tuple contains the class and its score.

Examples::
>>> from imgutils.validate import anime_teen
>>>
>>> anime_teen('teen/contentious/1.jpg')
('contentious', 0.9998493194580078)
>>> anime_teen('teen/contentious/2.jpg')
('contentious', 0.9790042638778687)
>>> anime_teen('teen/contentious/3.jpg')
('contentious', 0.9998124241828918)
>>> anime_teen('teen/safe_teen/4.jpg')
('safe_teen', 0.9989691972732544)
>>> anime_teen('teen/safe_teen/5.jpg')
('safe_teen', 0.9994398951530457)
>>> anime_teen('teen/safe_teen/6.jpg')
('safe_teen', 0.9994019269943237)
>>> anime_teen('teen/non_teen/7.jpg')
('non_teen', 0.999934196472168)
>>> anime_teen('teen/non_teen/8.jpg')
('non_teen', 0.9998156428337097)
>>> anime_teen('teen/non_teen/9.jpg')
('non_teen', 0.9997410178184509)