Sovit Rath
Traditionally, Deep-Learning models are trained on high-end GPUs. But for inference, Intel CPUs and edge devices like NVidia’s Jetson and Intel-Movidius VPUs are preferred. Most of these Intel CPUs come bundled with an integrated GPU, which though is not
Sovit Rath
Deep Learning models inferencing on video stream inputs in computer vision applications are mostly used for object detection, image segmentation, and image classification. In many cases, we fail to get high FPS while carrying out these tasks. But what
Sovit Rath
The training of neural-network architectures is what drives most of us who are involved in the field of Deep Learning. We fixate endlessly over the amount of data, its quality, and what neural network architectures we should use. But
Bibin Sebastian
This exciting post comes to you from Bibin Sebastian, who took our Deep Learning with Pytorch course by OpenCV, and then applied all the learning to create a Human Action Recognition application using PyTorch. Isn’t that amazing? Let’s read on to
Aditya Sharma
Continuing our Generative Adversarial Network a.k.a. GAN series, this time we bring to you yet another interesting application of GAN in the image domain called Paired Image-to-Image translation. By now, you have seen many different types of GANs, all
- Tags: CGAN, Multi-GPU, Pix2Pix, PyTorch, Tensorflow 2, tensorflow.keras, torch.nn, UNET
Aditya Sharma
Our last couple of posts have thrown light on an innovative and powerful generative-modeling technique known as Generative Adversarial Network (GAN). Yes, the GAN story started with the vanilla GAN. But no, it did not end with the Deep
