metadata

license: afl-3.0
tags:
  - feature_extraction
  - image
  - perceptual_metric
datasets:
  - tid2008
  - tid2013
metrics:
  - pearsonr
model-index:
  - name: PerceptNet
    results:
      - task:
          type: feature_extraction
          name: Perceptual Distance
        dataset:
          type: image
          name: tid2013
        metrics:
          - type: pearsonr
            value: 0.93
            name: PearsonR (MOS)

PerceptNet

PercepNet model trained on TID2008 and validated on TID2013, obtaining 0.97 and 0.93 Pearson Correlation respectively.

Link to the run: https://wandb.ai/jorgvt/PerceptNet/runs/28m2cnzj?workspace=user-jorgvt

Usage

As of now to use the model you have to install the PerceptNet repo to get access to the PerceptNet class where you will load the weights available here like this:

from perceptnet.networks import PerceptNet
from tensorflow.keras.utils import get_file

weights_path = get_file(fname='perceptnet_rgb.h5',
                        origin='https://huggingface.co/Jorgvt/PerceptNet/resolve/main/tf_model.h5')
model = PerceptNet(kernel_initializer='ones', gdn_kernel_size=1, learnable_undersampling=False)
model.build(input_shape=(None, 384, 512, 3))
model.load_weights(weights_path)

PerceptNet requires wandb to be installed. It's something we're looking into.