The facial depth estimation deep neural network, can estimate the depth for each pixel in terms of distance to the camera. Normally to do this you would need an extra depth sensor on your camera or need to take a pair of images of the face. Our deep neural network model can estimate the depth using just a single RGB input image. It does not need any additional information.
You have total freedom in how you want to deploy the model. You can put it on a server, public or private cloud. Or alternatively use it directly in your application or on an edge device. This choice is totally up to you. There are no constraints and you do not pay any transaction fees for using the model from Mut1ny
Deliverable in these supported neural network formats*:
*In your order please use the additional field to indicate which neural network format you would prefer
What is included in the facial depth estimation deep neural network?
- The model in the network framework format of your choice (see above)
- For ONNX format only:
- For PyTorch only:
- For mxnet only
Why buy a deep neural network model? When you can build and train one yourself? Or get one free from the internet?
- Training a segmentation model from scratch using a large enough dataset takes between 2-3 days even with a high-end equipped GPU. This translates to € 80-150,- public cloud instance spending costs alone.
- No need for you to spend development time instead you can fully concentrate on your actual product feature.
- Getting a dataset like this is very hard.
Why go for a subscription instead of single one-off purchase?
- Subscription allows you take advantage of Mut1ny’s future neural network model improvements. We improve our neural network models on a permanent basis adopting to latest research developments. This mean you benefit from our research and therefore will get better models over time.
- Subscription allows you take advantage of Mut1ny’s constantly growing training set. We constantly enlarge our training set with new data to cover a wider variety. Also if you are subscription user and you encounter cases that do not produce a result to our satisfactions you can send them to us. We’ll be including those in our training set.