Skip to content

kozobot/deinked

Repository files navigation

deinked

Port of https://github.com/vijishmadhavan/SkinDeep from FastAI 1 to FastAI 2.

Running Deinked

Locally

To run the Jupyter Notebooks I use a an extended version of the FastAI docker containers. https://github.com/fastai/docker-containers. The changes I've made are:

  • Changed the root from fastai to local
  • Changed the base ubuntu image to an NVIDIA/CUDA one (nvidia/cuda:11.3.1-base-ubuntu20.04) for GPU training
  • Added an image (-ext) with my dependencies and endpoint
    • Locally install ffmpeg libsm6 libxext6
    • Pip install nvidia-ml-py3 opencv-python Pillow

This is the command that I use to run the customized container.

docker run --rm \
    --gpus all --privileged \
    --name fastai --ipc=host \
    -p 8888:8888 \
    -v `pwd`:/home \
    local/fastai-ext \
    jupyter notebook

Collab

Haven't set this up yet

Setting everything up

Prepare the training data

Place your training set in data/rawdata. The tattoo image and clean image should be named _tattoo.jpeg and _clean.jpeg respectively.

Run the Deink - Process Raw Data notebook. This will prep the images for training and put them in data/tattoo and data/clean respectively.

Running training

The Deink - Train Model notebook, unsurprisingly, is for training the model. There is a cell for setting training variables.

Predicting images

Use the Deink - Predict Image notebook to load the model and process an image of your choosing.

About

Automated image tattoo removal via AI magic

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published