PC-Based Deep Learning
With PC-based deep learning a short product development time and low integration costs are possible. In addition, PC-based systems score with a simple and convenient design-in.
Deep Learning in Image Processing
The following topics are covered in detail in our White Paper:
Fields of application of deep learning in machine vision
Advantages of Deep Learning-Based Methods
Cost of using deep learning
Optimization of deep learning networks through hybrid approach
Fields of application for solutions without deep learning
Choose from our camera portfolio for your deep learning application
Basler’s broad range of cameras makes it very easy to choose the right camera for a specific deep learning vision system. The artificial neural Network (ANN) can only perform as well as the quality of data that it gets as input. Therefore, image quality plays a significant role in designing a robust deep learning vision system. Find the suitable Basler ace or Basler MED ace for your application.
The advantages of Basler products for PC-based applications are:
Basler offers a large variety of cameras with many different CCD and CMOS image sensors to fit the needs of any specific deep learning vision application.
Basler supports all data interfaces for PC-based setups, such as GigE and USB 3.0.
Most Basler cameras have in-camera image processing, which reduces the need for additional image pre-processing, such as debayering, sharpness enhancement, noise reduction etc. on the PC side.
Basler cameras deliver extremely reproducible results.
Use pylon for your deep learning applications
High-performance drivers, easy-to-learn programming interfaces and the large number of supported camera interfaces characterize the Basler pylon Camera Software Suite. This makes it ideally suited for easy and stable integration of Basler cameras into your deep learning applications.
Stable and efficient connection of Basler cameras
Easy-to-learn APIs: fast results, high productivity
Available for Windows, Linux x86, Linux ARM, macOS and Android