There are two alternatives to use the model: install our development repo and load the pretrained weights manually, and load the model using from_pretrained_keras:

Loading weights manually

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:

python

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.

Directly from the Hub

As every other Keras model in the Hub, it can be loaded as follows:

python

from huggingface_hub import from_pretrained_keras
model = from_pretrained_keras("Jorgvt/PerceptNet", compile=False)

Keep in mind that the model uses grouped convolutions and, at least in Colab, Unimplemented Errors may arise when using it in CPU.