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--- |
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license: afl-3.0 |
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tags: |
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- feature_extraction |
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- image |
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- perceptual_metric |
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datasets: |
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- tid2008 |
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- tid2013 |
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metrics: |
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- pearsonr |
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model-index: |
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- name: PerceptNet |
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results: |
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- task: |
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type: feature_extraction |
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name: Perceptual Distance |
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dataset: |
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type: image |
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name: tid2013 |
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metrics: |
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- type: pearsonr |
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value: 0.93 |
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name: PearsonR (MOS) |
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--- |
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# PerceptNet |
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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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from tensorflow.keras.utils import get_file |
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weights_path = get_file(fname='perceptnet_rgb.h5', |
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origin='https://huggingface.co/Jorgvt/PerceptNet/resolve/main/tf_model.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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``` |
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> PerceptNet requires `wandb` to be installed. It's something we're looking into. |
