Developing and Deploying Your Custom Action Recognition Application Without Any AI Expertise Using NVIDIA TAO

Build an action recognition app with pretrained models, the TAO Toolkit, and DeepStream without large training data sets or deep AI expertise.

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NVIDIA
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Chintan Shah
•14 min read•advanced•
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Overview

This article provides a comprehensive guide on developing and deploying a custom action recognition application using NVIDIA's TAO Toolkit and DeepStream SDK, emphasizing that no AI expertise is required. It outlines the workflow from fine-tuning a pretrained model to deploying it for inference, making it accessible for users looking to implement AI solutions in various fields.

What You'll Learn

1

How to fine-tune a pretrained action recognition model using the TAO Toolkit

2

Why using transfer learning can expedite AI model development

3

How to deploy a custom action recognition model using DeepStream

4

When to use different sampling strategies for model evaluation

Prerequisites & Requirements

  • NVIDIA GPU Driver version: >470
  • NVIDIA Docker: 2.5.0-1
  • NVIDIA TAO Toolkit: 3.0-21-11
  • NVIDIA DeepStream: 6.0
  • NVIDIA GPU in the cloud or on-premises (A100, V100, T4, RTX 30×0)

Key Questions Answered

What is the process for fine-tuning a pretrained action recognition model?
The process involves using the TAO Toolkit to fine-tune a pretrained model with custom data, configuring training parameters, and executing training commands. This allows users to adapt the model to specific classes and actions efficiently, leveraging transfer learning to reduce the amount of data and time needed compared to training from scratch.
What are the expected inference performance metrics for action recognition models?
The expected inference performance varies by model and GPU. For example, the 2D ResNet18 model achieves 30 FPS on the Nano, while the A100 GPU can reach up to 10,457 FPS. The 3D model shows lower performance, with the A100 achieving 640 FPS, indicating that model complexity affects inference speed.
How does the TAO Toolkit simplify AI model development?
The TAO Toolkit abstracts the complexities of AI and deep learning frameworks, allowing users to create production-ready models without requiring deep AI expertise. It provides a user-friendly CLI and Jupyter notebook interface for training and fine-tuning models, making it accessible for developers.
What are the steps to evaluate a trained action recognition model?
To evaluate a trained model, you can use sampling strategies like center mode or conv mode to assess performance on video clips. The evaluation process involves using a spec file to configure the evaluation parameters and running the evaluation command to obtain accuracy metrics for the trained classes.

Key Statistics & Figures

2D model accuracy
83%
Achieved on the pretrained action recognition model trained on the HMDB51 dataset.
3D model accuracy
86%
Achieved on the pretrained action recognition model trained on the HMDB51 dataset.
Inference performance on NVIDIA A100
10,457 FPS for 2D model, 640 FPS for 3D model
Demonstrates the performance capabilities of the A100 GPU when running the action recognition models.

Technologies & Tools

Software
Nvidia Tao Toolkit
Used for fine-tuning pretrained models and simplifying AI model development.
Software
Nvidia Deepstream
Used for deploying the trained action recognition model for inference.

Key Actionable Insights

1
Utilize the pretrained action recognition model from the NGC catalog to save time on development.
Starting with a pretrained model allows you to leverage existing training efforts and focus on fine-tuning it with your specific data, significantly reducing the time and resources needed for model development.
2
Experiment with different sampling strategies during model evaluation to find the best fit for your application.
Choosing the right evaluation strategy can impact the accuracy of your model's predictions. Testing both center mode and conv mode can help you understand which method yields better results for your specific use case.
3
Ensure your training dataset is well-prepared and follows the required directory structure for optimal results.
A properly structured dataset is crucial for the training process. Following the guidelines for data organization will help avoid errors and ensure that the model can effectively learn from the provided examples.

Common Pitfalls

1
Neglecting to properly configure the training parameters can lead to suboptimal model performance.
Without careful tuning of hyperparameters like learning rate and batch size, the model may not converge effectively, resulting in lower accuracy and longer training times.
2
Failing to preprocess the dataset correctly can cause errors during training.
If the dataset is not structured according to the expected format, the training process may fail, leading to wasted time and resources. Always verify the dataset organization before starting training.

Related Concepts

Transfer Learning
Deep Learning Frameworks
Video AI Applications
Temporal Action Recognition