
# 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

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](https://github.com/Jorgvt/perceptnet) 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.
{"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": {"name": "tid2013", "type": "image"}, "metrics": [{"type": "pearsonr", "value": 0.93, "name": "PearsonR (MOS)"}]}]}]}
keras tf feature_extraction image perceptual_metric dataset:tid2008 dataset:tid2013 license:afl-3.0 model-index region:us
TAGS
#keras #tf #feature_extraction #image #perceptual_metric #dataset-tid2008 #dataset-tid2013 #license-afl-3.0 #model-index #region-us 


# PerceptNet

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

Link to the run: URL

# Usage

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:



> 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:



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

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

Link to the run: URL # Usage

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:



> 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:



> Keep in mind that the model uses grouped convolutions and, at least in Colab, 'Unimplemented Errors' may arise when using it in CPU.
TAGS
#keras #tf #feature_extraction #image #perceptual_metric #dataset-tid2008 #dataset-tid2013 #license-afl-3.0 #model-index #region-us 
 # PerceptNet

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

Link to the run: URL # Usage

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:



> 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:



> Keep in mind that the model uses grouped convolutions and, at least in Colab, 'Unimplemented Errors' may arise when using it in CPU.
passage: TAGS
#keras #tf #feature_extraction #image #perceptual_metric #dataset-tid2008 #dataset-tid2013 #license-afl-3.0 #model-index #region-us 
# PerceptNet

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

Link to the run: URL# Usage

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:



> 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:



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