This 2 class NSFW-detector is a lightweight Autokeras model that takes CLIP ViT L/14 embbedings as inputs. It estimates a value between 0 and 1 (1 = NSFW) and works well with embbedings from images.
DEMO-Colab: https://colab.research.google.com/drive/19Acr4grlk5oQws7BHTqNIK-80XGw2u8Z?usp=sharing
The training CLIP V L/14 embbedings can be downloaded here: https://drive.google.com/file/d/1yenil0R4GqmTOFQ_GVw__x61ofZ-OBcS/view?usp=sharing (not fully manually annotated so cannot be used as test)
The (manually annotated) test set is there https://github.com/LAION-AI/CLIP-based-NSFW-Detector/blob/main/nsfw_testset.zip
https://github.com/rom1504/embedding-reader/blob/main/examples/inference_example.py inference on laion5B
Example of use of the model:
@lru_cache(maxsize=None)
def load_safety_model(clip_model):
"""load the safety model"""
import autokeras as ak # pylint: disable=import-outside-toplevel
from tensorflow.keras.models import load_model # pylint: disable=import-outside-toplevel
cache_folder = get_cache_folder(clip_model)
if clip_model == "ViT-L/14":
model_dir = cache_folder + "/clip_autokeras_binary_nsfw"
dim = 768
elif clip_model == "ViT-B/32":
model_dir = cache_folder + "/clip_autokeras_nsfw_b32"
dim = 512
else:
raise ValueError("Unknown clip model")
if not os.path.exists(model_dir):
os.makedirs(cache_folder, exist_ok=True)
from urllib.request import urlretrieve # pylint: disable=import-outside-toplevel
path_to_zip_file = cache_folder + "/clip_autokeras_binary_nsfw.zip"
if clip_model == "ViT-L/14":
url_model = "https://raw.githubusercontent.com/LAION-AI/CLIP-based-NSFW-Detector/main/clip_autokeras_binary_nsfw.zip"
elif clip_model == "ViT-B/32":
url_model = (
"https://raw.githubusercontent.com/LAION-AI/CLIP-based-NSFW-Detector/main/clip_autokeras_nsfw_b32.zip"
)
else:
raise ValueError("Unknown model {}".format(clip_model)) # pylint: disable=consider-using-f-string
urlretrieve(url_model, path_to_zip_file)
import zipfile # pylint: disable=import-outside-toplevel
with zipfile.ZipFile(path_to_zip_file, "r") as zip_ref:
zip_ref.extractall(cache_folder)
loaded_model = load_model(model_dir, custom_objects=ak.CUSTOM_OBJECTS)
loaded_model.predict(np.random.rand(10**3, dim).astype("float32"), batch_size=10**3)
return loaded_model
nsfw_values = safety_model.predict(embeddings, batch_size=embeddings.shape[0])This code and model is released under the MIT license:
Copyright 2022, Christoph Schuhmann
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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