This book introduces you to Deep Learning and explains all the concepts required to understand the basic working, development, and tuning of a neural network using Pytorch.

This book is a valuable resource for professionals, researchers, and students who want to expand their knowledge of advanced computer vision techniques using PyTorch.

... depth map also has temporal consistency when output , we use the trained flow estimation network , FlowNet [ 4 ] ... monocular depth maps . All loss functions are combined as : 4 Lfinal = λdLd + λgLgrad + XƒLƒ where Ad , Ag , Aƒ are ...

... estimation of stereo videos with spatiotemporal loop consistency . IEEE Robot . Autom . Lett . 5 ( 4 ) , 5597-5604 ... depth prediction with fully convolutional residual networks . In : 3DV . IEEE ( 2016 ) 14. Li , X. , Chen , H ...

This book begins with an explanation of what anomaly detection is, what it is used for, and its importance.

After reading this book, you will be able to build your own computer vision projects using transfer learning and PyTorch. What You Will Learn Solve problems in computer vision with PyTorch.

... Monocular Depth Estimation challenge results and final rankings . The runtime values were obtained on 640 × 480 px ... PyTorch / TensorFlow 1.5 PyTorch / TensorFlow 1.0 0.311 Model Size , MB si - RMSE ↓ RMSE ↓ LOG10 ↓ REL ...

... monocular visual odometry and depth estimation approach for endoscopic videos. Med. Image Anal. 71, 102058 (2021) 11. Paszke, A., et al.: Automatic differentiation in pytorch (2017) 12. Shao, S., et al.: Self-supervised monocular depth ...

... depth consistency loss . 4 Experiments 4.1 Implementation Details Table 1. Quantitative monocular depth estimation results on the KITTI. Our proposed method is implemented in PyTorch [ 30 ] and trained on a single Titan RTX GPU . Our ...