This walkthrough shares how a user can quickly build and deploy a computer vision application with the NVIDIA NGC catalog and Google Vertex AI.
Overview
This article discusses building a computer vision application to recognize human activities using NVIDIA AI software and Google Cloud Vertex AI. It highlights the advancements in computer vision models, the benefits of transfer learning, and provides a step-by-step guide to deploying an action recognition application.
What You'll Learn
1
How to build and deploy a computer vision application using NVIDIA AI software
2
Why transfer learning is beneficial for developing custom models with limited resources
3
How to fine-tune a pretrained action recognition model using the HMDB51 dataset
Prerequisites & Requirements
- Basic understanding of computer vision concepts
- Access to NVIDIA NGC catalog and Google Cloud Vertex AI
Key Questions Answered
What resources are needed to build a computer vision application using NVIDIA AI?
To build a computer vision application using NVIDIA AI, you need access to the NGC catalog for GPU-optimized AI frameworks, the NVIDIA TAO Toolkit for model adaptation, and a Google Cloud account with Vertex AI Workbench for deployment.
How does the quick deploy feature in Vertex AI Workbench simplify application development?
The quick deploy feature allows users to launch a JupyterLab instance with optimal configurations, preload software dependencies, and download NGC notebooks in one step, significantly reducing setup time and complexity.
What dataset is used for fine-tuning the action recognition model?
The HMDB51 dataset is used for fine-tuning the action recognition model, which includes various human actions, allowing the model to learn and predict actions like 'fall-floor' and 'ride-bike'.
What are the two modes for evaluating the pretrained model?
The two evaluation modes for the pretrained model are 'center mode', which picks the middle frames of a video sequence for inference, and 'conv mode', which samples multiple sequences from a video and averages the results.
Technologies & Tools
Software
Nvidia Tao Toolkit
Used for fine-tuning pretrained models with custom data.
Cloud Platform
Google Cloud Vertex AI
Provides a development environment for building and deploying AI models.
Dataset
Hmdb51
Dataset used for training and evaluating the action recognition model.
Key Actionable Insights
1Utilize the quick deploy feature in Vertex AI Workbench to streamline your development process.This feature simplifies the setup of your development environment, allowing you to focus on building your application rather than configuring infrastructure.
2Leverage transfer learning to reduce the resources needed for model training.By using pretrained models, you can achieve high accuracy with significantly less training data and computational power, making it accessible for developers with limited resources.
3Experiment with different evaluation modes to optimize model performance.Understanding how to effectively evaluate your model can help you refine its accuracy and ensure it performs well in real-world scenarios.
Common Pitfalls
1
Failing to properly configure environment variables can lead to issues when running the Jupyter notebook.
Ensure that all paths and keys are correctly set before executing code cells to avoid runtime errors.
2
Not utilizing the pretrained models effectively may result in longer training times and lower accuracy.
Leveraging pretrained models can drastically reduce the time and resources needed for training, so it's essential to understand how to integrate them into your workflow.
Related Concepts
Transfer Learning
Action Recognition
Nvidia Ngc Catalog
Google Cloud AI Services