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  PercepNet model trained on TID2008 and validated on TID2013, obtaining 0.97 and 0.93 Pearson Correlation respectively.
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- Link to the run: https://wandb.ai/jorgvt/PerceptNet/runs/28m2cnzj?workspace=user-jorgvt
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  PercepNet model trained on TID2008 and validated on TID2013, obtaining 0.97 and 0.93 Pearson Correlation respectively.
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+ Link to the run: https://wandb.ai/jorgvt/PerceptNet/runs/28m2cnzj?workspace=user-jorgvt
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+ # Usage
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+ As of now to use the model you have to install the [PerceptNet repo](https://github.com/Jorgvt/perceptnet) to get access to the `PerceptNet` class where you will load the weights available here like this:
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+ ```python
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+ from perceptnet.networks import PerceptNet
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+
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+ weights_path = get_file(fname='perceptnet_rgb.h5',
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+ origin='https://huggingface.co/Jorgvt/PerceptNet/blob/main/final_model_rgb.h5')
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+ model = PerceptNet(kernel_initializer='ones', gdn_kernel_size=1, learnable_undersampling=False)
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+ model.build(input_shape=(None, 384, 512, 3))
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+ model.load_weights(weights_path)
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+ ```