DeepCreamPy & Hent-AI Guide: Installation and anime censorship removal (Version 2)

This page summarizes the projects mentioned and recommended in the original post on /r/AnimeResearch

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  • CenDetect

    Discontinued A repository to detect degradation in images and masking such areas.

    Check out CenDet's GitHub page for more information.

  • DeepCreamPy-archived

    Discontinued Archived version of DeepCreamPy.

    When in doubt, consult the troubleshooting guide.

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  • Mask_RCNN

    Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

    It is important to realize that to do its masking procedures, Hent-AI uses the Mask RCNN (MRCNN) package from Matterport. The problem with this version of MRCNN is that it is not compatible with Tensorflow 2.X versions, essentially limiting Hent-AI compatibility to strict Tensorflow 1.X versions. Since Tensorflow 1.15 is the last of the Tensorflow 1.X versions and uses CUDA 10.0, which supports a maximum compute capability of 7.5, this means that the last NVIDIA GPU series that is compatible with the original Hent-AI implementation is the RTX 2000 series. This is, of course, not optimal since it means that RTX 3000 series and later GPUs cannot be used despite their significant computing power and high VRAM.

  • Mask_RCNN_tf_2.x

    It is modified tensorflow 2.x version of mask-rcnn.

    Thus, if we want to use RTX 3000 series and later, we need to find a MRCNN that is Tensorflow 2.X compatible. Instead of updating the code myself, I looked through GitHub to see if anyone else had done this already. After some searching, I found a MRCNN package by BupyeongHealer that is compatible with Tensorflow 2.X versions. I implemented this package in Hent-AI by replacing the “mrcnn” folder (which has Matterport’s MRCNN) with the “mrcnn” folder from BupyeongHealer. Running Hent-AI at this point led to errors if trying to run Tensorflow 2.5 or newer due to the Layers class in Keras being moved from the Engine to the Layers module from Tensorflow 2.4 to 2.5, and Keras being moved from standalone to being part of the Tensorflow package itself. These errors were all eliminated by making the following modifications to “model.py” in the “mrcnn” folder:

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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