OpenVINO vs TensorFlow: What are the differences?

Introduction

TensorFlow and OpenVINO are both popular frameworks used for deep learning and computer vision tasks. While they have some similarities, there are also several key differences between them. This article will highlight and explain these differences.

1. Model Training vs Model Optimization: One major difference between TensorFlow and OpenVINO is their primary focus. TensorFlow is designed for model training, providing a wide range of tools and functionalities for developing and training deep learning models. On the other hand, OpenVINO is primarily focused on model optimization and deployment, aiming to optimize and run pretrained models efficiently on various hardware platforms.

2. Backend Support: TensorFlow supports multiple backends, including CPUs, GPUs, and even specialized hardware like TPUs (Tensor Processing Units). It allows users to choose the backend based on their hardware resources and requirements. In contrast, OpenVINO mainly focuses on accelerated inference and provides extensive support for Intel hardware platforms, such as CPUs, integrated GPUs, and FPGAs (Field Programmable Gate Arrays).

3. Model Compatibility: TensorFlow has a wide range of pre-trained models available, including various architectures like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers. These models can be trained and fine-tuned using TensorFlow itself. OpenVINO, on the other hand, primarily focuses on optimizing and deploying models from other popular frameworks like TensorFlow, Caffe, and ONNX. It allows users to take advantage of the optimized performance of OpenVINO with models developed in other frameworks.

4. Performance Optimization: OpenVINO is specifically designed for optimizing the inference performance of deep learning models. It incorporates various techniques like model quantization, layer fusion, and kernel optimizations to achieve faster inference speed and reduced memory footprint. TensorFlow also provides optimization options, but the level of optimization offered by OpenVINO is more extensive and platform-specific.

5. Hardware Integration and Deployment: OpenVINO provides specialized tools and libraries that enable developers to deploy optimized models on specific Intel hardware platforms. It simplifies the deployment process and ensures better compatibility and performance with Intel hardware. TensorFlow, on the other hand, is more hardware-agnostic and can be used on a wide range of hardware platforms, not limited to Intel.

6. Community and Ecosystem: TensorFlow has a larger and more active community compared to OpenVINO. It has been widely adopted by researchers and developers, resulting in a rich ecosystem with numerous libraries, tools, and resources available. OpenVINO has a smaller community but still provides comprehensive documentation and support for users to utilize its optimization capabilities effectively.

In summary, TensorFlow is a versatile framework primarily focused on model training, while OpenVINO specializes in optimizing and deploying pretrained models on Intel hardware platforms. TensorFlow supports multiple backends, provides compatibility with various model architectures, and has a larger community and ecosystem. On the other hand, OpenVINO offers extensive performance optimization and hardware integration options specifically tailored to Intel platforms.