Content Moderation
- Text & image moderation on high profile dating sites
- Image filtering for user-generated content
- Selecting best images for marketing purposes
- GDPR compliance with automated data cleansing
Objective
Detection of inappropriate content is a mandatory requirement for most content platforms. Usually we refer to the detection of NOK (non-OK) images or text from OK content.
Below we show how DeepDetect is used to filter both text and images by leaders of content moderation and digital marketing companies.
Image moderation
Typical applications:
- NSFW detection, setup the existing NSFW model
- Detection of message passing in images, use the text detection model
- Detection of eyes, mouth, nose to make sure faces are facing the camera
- Detection of drinks, violence, …
Examples
Train your own model
We recommend training an image classification model with the platform. This is fairly simple:
Create two directories, one with the OK images, one with the NOK images. Thanks to the platform [pre-trained models](), you may not require more than a few hundreds images per directory
Train your model with the platform by following the image training instructions
Test and analyze results with the platform
Put into production by using the platform or [deploying with the DeepDetect server]().
Text moderation
Training
We recommend training a text classification model with the platform using a character-based neural network. This is a good choice for dealing with user-generated content since it can work around variations in spelling etc…
Building the model is fairly simple:
Create two directories, one with the OK text samples, one with the NOK samples. You may need a few hundreds to few thousands samples per directory, up to hundreds of thousands for best results if your problem is difficult.
Train your model with the platform by following the text training instructions
Test and analyze results with the platform
Put into production by using the platform or [deploying with the DeepDetect server]().