Chainer implementation for realismCNN.
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Chainer implementation for realismCNN proposed in Learning a Discriminative Model for the Perception of Realism in Composite Images.

Download pretrained caffe model

  1. Download pretrained caffe model.
  2. Run python to transform pretrained caffe model into Chainer model.

Predict image's realism

  1. Download dataset Realism Prediction Data.
  2. Run python to obtain image list & ground truth.
  3. Run python to obtain prediction results. AUC score will be printed out, prediction score for each image will be stored in plain text file.

Image Editing towards generating more realistic composited images

  1. Download dataset Color Adjustment Data.
  2. Run python to obtain image list.
  3. Run python to obtain more realistic images. (cut_and_paste image, generated image) will be saved in the result folder, and a plain file will be generated recording (cut_and_paste loss, generated loss) for each image.







  • Run python [SCRIPT_NAME].py -h for more options.
  • Download converted Chainer VGG-19 model from here.
  • If you want to download Transient Attributes Dataset, please see the project website for more details