dev(narugo): add more docs for dbrating

docs/source/api_doc/validate/dbrating.rst

@@ -0,0 +1,21 @@
+imgutils.validate.dbrating
+=============================================
+
+.. currentmodule:: imgutils.validate.dbrating
+
+.. automodule:: imgutils.validate.dbrating
+
+
+anime_dbrating_score
+-----------------------------
+
+.. autofunction:: anime_dbrating_score
+
+
+
+anime_dbrating
+-----------------------------
+
+.. autofunction:: anime_dbrating
+
+

docs/source/api_doc/validate/index.rst

@@ -14,6 +14,7 @@ imgutils.validate
     classify
     color
     completeness
+    dbrating
     monochrome
     nsfw
     portrait

imgutils/validate/dbrating.py

@@ -57,6 +57,42 @@ def anime_dbrating_score(image: ImageTyping, model_name: str = _DEFAULT_MODEL_NA
         If you need better accuracy, just set this to ``caformer_s36_v0_ls0.2``.
     :return: A dict with ratings and scores.
 
+    Examples::
+        >>> from imgutils.validate import anime_dbrating
+        >>>
+        >>> anime_dbrating('general/1.jpg')
+        ('general', 0.7508869767189026)
+        >>> anime_dbrating('general/2.jpg')
+        ('general', 0.7034655809402466)
+        >>> anime_dbrating('general/3.jpg')
+        ('general', 0.728887677192688)
+        >>> anime_dbrating('general/4.jpg')
+        ('general', 0.7404400110244751)
+        >>> anime_dbrating('sensitive/5.jpg')
+        ('sensitive', 0.7446154952049255)
+        >>> anime_dbrating('sensitive/6.jpg')
+        ('sensitive', 0.7514738440513611)
+        >>> anime_dbrating('sensitive/7.jpg')
+        ('sensitive', 0.768704354763031)
+        >>> anime_dbrating('sensitive/8.jpg')
+        ('sensitive', 0.8219676613807678)
+        >>> anime_dbrating('questionable/9.jpg')
+        ('questionable', 0.7267540693283081)
+        >>> anime_dbrating('questionable/10.jpg')
+        ('questionable', 0.7645740509033203)
+        >>> anime_dbrating('questionable/11.jpg')
+        ('questionable', 0.7216582894325256)
+        >>> anime_dbrating('questionable/12.jpg')
+        ('questionable', 0.7615436315536499)
+        >>> anime_dbrating('explicit/13.jpg')
+        ('explicit', 0.815083920955658)
+        >>> anime_dbrating('explicit/14.jpg')
+        ('explicit', 0.8321858644485474)
+        >>> anime_dbrating('explicit/15.jpg')
+        ('explicit', 0.8204999566078186)
+        >>> anime_dbrating('explicit/16.jpg')
+        ('explicit', 0.820833146572113)
+
     """
     return classify_predict_score(image, _REPO_ID, model_name)
 
@@ -71,6 +107,42 @@ def anime_dbrating(image: ImageTyping, model_name: str = _DEFAULT_MODEL_NAME) ->
         on the benchmark plot above. If you need better accuracy, just set this to ``caformer_s36_v0_ls0.2``.
     :return: A tuple contains the rating and its score.
 
-
+    Examples::
+        >>> from imgutils.validate import anime_dbrating_score
+        >>>
+        >>> os.chdir('docs/source/api_doc/validate/dbrating')
+        >>>
+        >>> anime_dbrating_score('general/1.jpg')
+        {'general': 0.7508870363235474, 'sensitive': 0.11212056130170822, 'questionable': 0.06781744956970215, 'explicit': 0.06917501986026764}
+        >>> anime_dbrating_score('general/2.jpg')
+        {'general': 0.7034654021263123, 'sensitive': 0.15903906524181366, 'questionable': 0.06688199192285538, 'explicit': 0.07061357796192169}
+        >>> anime_dbrating_score('general/3.jpg')
+        {'general': 0.7288877964019775, 'sensitive': 0.1476859599351883, 'questionable': 0.060362350195646286, 'explicit': 0.06306383013725281}
+        >>> anime_dbrating_score('general/4.jpg')
+        {'general': 0.7404399514198303, 'sensitive': 0.10337048768997192, 'questionable': 0.08087948709726334, 'explicit': 0.07530999928712845}
+        >>> anime_dbrating_score('sensitive/5.jpg')
+        {'general': 0.055992450565099716, 'sensitive': 0.7446154356002808, 'questionable': 0.13191790878772736, 'explicit': 0.06747424602508545}
+        >>> anime_dbrating_score('sensitive/6.jpg')
+        {'general': 0.06458679586648941, 'sensitive': 0.7514738440513611, 'questionable': 0.10566363483667374, 'explicit': 0.07827574014663696}
+        >>> anime_dbrating_score('sensitive/7.jpg')
+        {'general': 0.07079866528511047, 'sensitive': 0.7687042951583862, 'questionable': 0.09974884241819382, 'explicit': 0.06074819341301918}
+        >>> anime_dbrating_score('sensitive/8.jpg')
+        {'general': 0.050435908138751984, 'sensitive': 0.8219675421714783, 'questionable': 0.0593985915184021, 'explicit': 0.06819795072078705}
+        >>> anime_dbrating_score('questionable/9.jpg')
+        {'general': 0.06569571048021317, 'sensitive': 0.1177448257803917, 'questionable': 0.726753830909729, 'explicit': 0.08980562537908554}
+        >>> anime_dbrating_score('questionable/10.jpg')
+        {'general': 0.06481882929801941, 'sensitive': 0.06922297924757004, 'questionable': 0.7645740509033203, 'explicit': 0.10138414055109024}
+        >>> anime_dbrating_score('questionable/11.jpg')
+        {'general': 0.06351721286773682, 'sensitive': 0.07683827728033066, 'questionable': 0.7216582894325256, 'explicit': 0.13798624277114868}
+        >>> anime_dbrating_score('questionable/12.jpg')
+        {'general': 0.05942752957344055, 'sensitive': 0.10584963858127594, 'questionable': 0.7615437507629395, 'explicit': 0.07317910343408585}
+        >>> anime_dbrating_score('explicit/13.jpg')
+        {'general': 0.060196295380592346, 'sensitive': 0.06751583516597748, 'questionable': 0.0572039857506752, 'explicit': 0.815083920955658}
+        >>> anime_dbrating_score('explicit/14.jpg')
+        {'general': 0.05398125201463699, 'sensitive': 0.06124086305499077, 'questionable': 0.0525919646024704, 'explicit': 0.8321859240531921}
+        >>> anime_dbrating_score('explicit/15.jpg')
+        {'general': 0.05922013148665428, 'sensitive': 0.06274889409542084, 'questionable': 0.057530902326107025, 'explicit': 0.8205001354217529}
+        >>> anime_dbrating_score('explicit/16.jpg')
+        {'general': 0.05683052912354469, 'sensitive': 0.06635929644107819, 'questionable': 0.05597696080803871, 'explicit': 0.8208332657814026}
     """
     return classify_predict(image, _REPO_ID, model_name)