A PyTorch Implementation of Neural IMage Assessment
NIMA: Neural IMage Assessment
This is a PyTorch implementation of the paper NIMA: Neural IMage Assessment (accepted at IEEE Transactions on Image Processing) by Hossein Talebi and Peyman Milanfar. You can learn more from this post at Google Research Blog.
Implementation Details
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The model was trained on the AVA (Aesthetic Visual Analysis) dataset containing 255,500+ images. You can get it from here.
Note: there may be some corrupted images in the dataset, remove them first before you start training.Use provided CSVs which have already done this for you. -
Dataset is split into 229,981 images for training, 12,691 images for validation and 12,818 images for testing.
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An ImageNet pretrained VGG-16 is used as the