Unleash the Power of Semantic Segmentation Models

Table of Contents

1. Introduction
2. Common Challenges of Training and Deploying Segmentation Models
   1. Lack of Resources for Semantic Segmentation Models
   2. Non-production Oriented Implementations
   3. Challenges with Semantic Segmentation Models
3. Super Gradients: Training State-of-the-Art Segmentation Models
   1. Introduction to Super Gradients
   2. Benefits of Using Super Gradients
   3. Training Techniques and Best Practices
      1. Exponential Moving Average
      2. Learning Rate Scheduler
      3. Loss Functions
      4. Augmented Auxiliaries Head Losses
   4. Integrated State-of-the-Art Segmentation Models
4. Customizing Architectures with Desi Platform
   1. Introduction to the Desi Platform
   2. Autonomic Engine: Hardware-Aware NAS
   3. Use Cases and Results of Customized Segmentation Models
5. Conclusion
6. FAQs
   1. Do you have implementations for Salient object detection?
   2. Do you have pre-trained models for retail-specific foreground and background segmentation?
   3. How often is the upgrade for the GPU?
   4. What are the best practices for fast model inference on WebGL?

🌟 Achieving State-of-the-Art Accuracy and Runtime for Semantic Segmentation Models

In this article, we will explore the challenges faced in training and deploying semantic segmentation models and introduce a solution - Super Gradients. We will also discuss best practices for training semantic segmentation models and how to customize architectures using the Desi platform. So let's dive in!

Introduction

Semantic segmentation plays a crucial role in computer vision tasks by providing pixel-level categorization of images. However, training and deploying segmentation models can be a challenging task. In this article, we will address these challenges and provide insights into achieving state-of-the-art accuracy and runtime for semantic segmentation models.

Common Challenges of Training and Deploying Segmentation Models

Lack of Resources for Semantic Segmentation Models

Unlike other deep learning tasks, there is a lack of resources for semantic segmentation models. While there are existing repositories and libraries for segmentation, there is no reliable Consensus source for selecting, training, and deploying public models. This vacuum makes it difficult for developers to find and utilize the best-performing models for their use case.

Non-production Oriented Implementations

Many segmentation repositories and tools in the open-source community focus on implementation and training aspects but lack production-oriented features. This poses a challenge when deploying segmentation models in real-world production environments. Issues like compilation errors, large memory footprint, and high latency can arise when running complex neural architectures in production.

Challenges with Semantic Segmentation Models

Semantic segmentation models are often composed of large and complex neural architectures that may not run well in production environments. These models can encounter problems like compilation issues, large memory footprint, and high latency, making it difficult to meet real-time performance requirements. Additionally, selecting the right loss functions and training techniques for segmentation models can be a complex task.

Super Gradients: Training State-of-the-Art Segmentation Models

Introduction to Super Gradients

Super Gradients, an open-source computer vision training library, offers a comprehensive solution to the challenges faced in training semantic segmentation models. With Super Gradients, developers can easily train state-of-the-art public models and integrate them into their codebase. The library provides access to pre-trained models that deliver higher accuracy compared to other libraries.

Benefits of Using Super Gradients

By utilizing Super Gradients, developers can achieve better results faster while ensuring compatibility with different runtime formats like ONNX, OpenVINO, Core ML, and more. Super Gradients offers a wide range of segmentation models like DDNet, LeatherNet, RegSeg, STDC, and ShelfNet, which have proven to deliver excellent performance on datasets like Cityscapes and COCO.

Training Techniques and Best Practices

To get the most out of semantic segmentation models, Super Gradients incorporates various training techniques and best practices. These practices have been proven to improve model accuracy and reduce training time. Some of the techniques include:

Exponential Moving Average

Using the Exponential Moving Average (EMA) method helps enhance the stability of model convergence and improve overall solution quality. EMA updates the weights by considering both the current weights and the post-optimization step weights, resulting in better convergence and preventing local minima.

Learning Rate Scheduler

Super Gradients employs the Poly Learning Rate Scheduler, a common technique used in semantic segmentation. By utilizing linear warm-up and different learning rates for encoder and decoder models, the Intersection over Union (IoU) results can be improved, leading to more accurate segmentation.

Loss Functions

Super Gradients offers different loss functions that can be combined to achieve better coverage and accuracy. Cross-Entropy loss is the standard choice for semantic segmentation, but Super Gradients also implements other loss functions like Dice loss and Boundary loss, which improve local and global segmentation results.

Augmented Auxiliaries Head Losses

Augmented auxiliaries heads, extracted from intermediate encoder feature maps, provide additional supervision and lead to faster convergence and more stable training. These auxiliary heads improve the overall performance and decrease the training time.

Integrated State-of-the-Art Segmentation Models

Super Gradients integrates several state-of-the-art segmentation models, such as DDNet, LeatherNet, RegSeg, STDC, and ShelfNet families. These models have predefined recipes for datasets like Cityscapes and COCO. By using Super Gradients, developers can leverage these pre-trained models and achieve excellent results in terms of accuracy and runtime.

Customizing Architectures with Desi Platform

To further optimize segmentation models and tailor them to specific production needs, the Desi platform offers an efficient solution. Powered by Neural Architecture Search (NAS), the platform's Autonomic Engine enables data scientists to build customized architectures for accurate and efficient segmentation.

Introduction to the Desi Platform

The Desi platform provides a deep learning development environment where data scientists can build, optimize, and deploy models. The platform's Autonomic Engine, based on Neural Architecture Search, helps in constructing architectures tailored to specific tasks and inference hardware. By inputting production requirements and inference configurations, the Autonomic Engine searches for trainable architectures that meet the defined criteria.

Autonomic Engine: Hardware-Aware NAS

The Autonomic Engine takes into account hardware capabilities and performance requirements to create architectures that deliver the best accuracy and runtime. By utilizing Autonomic Engine, developers can achieve up to 5x better inference performance compared to state-of-the-art models while maintaining or improving accuracy.

Use Cases and Results of Customized Segmentation Models

The Desi platform has been successfully utilized in various use cases, resulting in significant improvements in performance and user experience. For example, a company needed to develop segmentation models for a video conference application running on different hardwares. With the Desi platform, they built a tailored architecture that seamlessly ran on different frameworks and hardwares, reducing costs and enhancing the user experience.

In another use case, a company wanted to deploy semantic segmentation models on mobile phones. They struggled to achieve the desired performance, especially on Android devices with Snapdragon chips. Using the Desi platform, they customized models for different architectures, achieving better latency and user experience while reducing costs.

Another customer, from the automotive domain, wanted to deploy segmentation models on Jetson Xavier NX for cars. With Desi's Autonomic Engine, they achieved a performance improvement of 2x, resulting in lower latency and better runtime performance.

Conclusion

In this article, we have explored the challenges of training and deploying semantic segmentation models and introduced Super Gradients and the Desi platform as solutions. By using Super Gradients, developers can train state-of-the-art segmentation models while following best practices for improved accuracy and training time. The Desi platform further enables customization of architectures to meet specific production needs and achieve better performance. With these tools, developers can unlock the full potential of semantic segmentation models and deliver high-quality computer vision applications.

Resources:

• Super Gradients: GitHub Repo
• Desi Platform: Website

FAQs

1. Q: Do you have implementations for salient object detection?

   • A: While our model zoo primarily focuses on segmentation models, we can utilize the Desi platform's Autonomic Engine to search for and build architectures tailored to salient object detection tasks.

2. Q: Do you have pre-trained models for retail-specific foreground and background segmentation?

   • A: Our pre-trained models currently cover a wide range of datasets, but we can work with custom datasets and train models specifically for retail foreground and background segmentation tasks.

3. Q: How often is the upgrade for the GPU?

   • A: GPU upgrades are dependent on the hardware provider and their release cycles. It's recommended to check with the respective GPU manufacturer or cloud service provider for the latest upgrades and offerings.

4. Q: What are the best practices for fast model inference on WebGL?

   • A: Achieving fast model inference on WebGL involves optimizing model size, reducing unnecessary calculations, and leveraging WebGL's Parallel computing capabilities. It's recommended to consult WebGL-specific resources and perform experimentations with model architectures and frameworks to achieve the best performance.

Please feel free to reach out to our team or join our community slack for more specific questions and assistance.