What is GPU Acceleration: A Data Science Powerhouse

You might hear about computer vision very often, especially for self-driving cars, medical image analysis, and various image recognition applications. Read this article to explore all the basic knowledge you need to know about computer vision, its applications in everyday life, as well as the challenges you may encounter during training CV models.

What is Computer Vision? How does it relate to AI?

Computer Vision is the ability of computers to recognize, analyze, and process visual contents using the way humans do. With AI technologies and algorithms, computers can learn to understand the patterns and traits of visual data. Once trained, computers can identify, classify, or even generate meaningful information from not only images, but also videos.

Deep Learning and Computer Vision

Convolutional Neural Network (CNN)

A Convolutional Neural Network is a specialized type of deep learning algorithm mainly designed for image classification, detection, and segmentation. Some major types of layers within convolutional neural network architecture include:

• Convolutional layer: The convolutional layer is the major building block of CNNs, where most of the computations occur. It applies a set of filters, also known as kernels, to the input image.
• Pooling layer: The pooling layer applies a filter aimed to decrease the size of the volume. While some information is lost during the pooling operation, it helps reduce the complexity and computations required, increasing the overall training efficiency while reducing the risk of overfitting.
• Fully connected (FC) layer: FC layer in CNN usually leverages functions like softmax activation function to perform the final classification or regression tasks based on features extracted from previous layers.

ResNET

ResNet, also known as Residual Network architecture, was introduced in 2015 to address the vanishing/exploding gradient issues in common CNN architectures, enabling the training of deeper networks.

Use Cases for Computer Vision

The potential of computer vision is vast, with applications spanning multiple industries. Some prominent use cases include:

Autonomous Vehicles

Computer vision enables self-driving cars and smart vehicles to perceive their surroundings, detect obstacles, recognize traffic signs, allowing them to make precise real time decisions.

Healthcare

From medical image analysis to surgical assistance, computer vision techniques ease the complexity of multiple medical processes, assisting medical professionals with image recognition, detection, and segmentation for greater precision and less invasiveness during procedure; detecting subtle signs of disease, and facilitating timely interventions.

Retail

Customer behavior analysis, product management, inventory management, and fraud prevention are some crucial applications of computer vision in the retail industry.

Challenges for Computer Vision

Data Variability and Image Quality

Images can vary greatly in lighting, angles, and occlusions, making it difficult for algorithms to achieve consistent accuracy. On the other hand, poor lighting, occlusions, and image noise can also hinder the performance. Therefore, advanced image preprocessing and algorithms robust to different lighting conditions become more and more important for CV applications.

I/O Bottlenecks

Large-scale computer vision model training is often hindered by challenges related to data storage, access, and management. Efficient access and process of massive datasets containing millions of images or videos are required. These processes demand high I/O throughput, low latency, and scalability as data size continues to grow. Traditional storage systems often struggle to meet these requirements, resulting in bottlenecks that slow down development cycles.

Computational Complexity

Training and deploying CV models require significant processing power, posing challenges for resource-constrained environments. In order to address the intensive needs of computational power and time, building scalable, reliable, and cost effective AI/ML infrastructures to accelerate GPUs with optimal utilization rate has become a top priority.

Accelerate your Computer Vision AI Workloads with Alluxio

While providing a scalable approach for image classification, recognition, and segmentation tasks, Computer Vision models can be computationally demanding, requiring significant amounts of GPUs to train the model.

Even with enough GPUs, slow data access still hindered them from running at their full potential, especially with exploding data volume in the computer vision field.

Alluxio Enterprise AI provides a solution to this challenge by enabling organizations to maximize the utilization of their existing GPU environments, enabling epic performance for your Computer Vision workload.

With Alluxio, data platform engineers can derive the full benefit from their GPU infrastructures, concentrate on improving their models and applications without being limited by poor storage performance, while ensuring their expensive GPU resources are fully utilized to their full potential.