Buy all the facial neural networks together in the facial deep neural networks bundle and save money while doing so.
This bundle includes:
- Facial feature segmentation neural network
- Facial ambient occlusion estimation neural network
- Facial depth estimation neural network
- Facial surface normal estimation neural network
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
The model is 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 bundle?
- All the models above 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 all these models from scratch with a decent amount of data takes between 2-3 days for each with a high-end equipped GPU which is between € 80-150,- public cloud instance running cost alone for each. Making it a combined cost of € 350-600.
- No need to spend any development time but instead you can fully concentrate on your product feature.
- Getting datasets for all these 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.