The pretrained model that is used in this resource is a classification network, which aims to classify human emotion into 6 categories.
Primary use case for this model is to detect human emotion. The model can be used to detect human emotion from photos and videos by using appropriate video or image decoding and pre-processing. The model takes in facial landmarks as input and provide emotion classes as output.
The quick deploy feature automatically sets up the Vertex AI instance with an optimal configuration, preloads the dependencies, runs the software from NGC without any need to set up the infrastructure.
To fine-tune and customize the model, you’ll be using the TAO (Train, Adapt and Optimize) Toolkit. The TAO Toolkit, a low-code AI model development solution, leverages the power of transfer learning to help fine-tune pretrained models with your own data. Transfer learning which is the process of transferring learned features from one application to another. It is a commonly used training technique where you use a model trained on one task and retrain to use it on a different task. With the TAO Toolkit, you can customize models for tasks in computer vision, natural language processing and speech.
Once you have customized the model, you can then use the built-in optimization techniques such as model pruning and quantization to optimize the model for inference on the target GPU, without sacrificing accuracy.
All the training steps are covered in the Jupyter notebook.
To help you get started, we have created a sample Jupyter Notebook that can be easily deployed on Vertex AI using NGC’s quick deploy feature. This feature automatically sets up the Vertex AI instance with an optimal configuration, preloads the dependencies, runs the software from NGC without any need to set up the infrastructure.
Simply click on the button that reads “Deploy to Vertex AI” and follow the instructions.
Note: A customized kernel for the Jupyter Notebook is used as the primary mechanism for deployment. This kernel has been built on the TAO Toolkit container. For more information on the container itself, please refer to this link for more information:
The container version: nvcr.io/nvidia/tao/tao-toolkit-tf:v3.21.11-tf1.15.5-py3