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[PaddleGAN Immersive space travel 👨‍🚀] Teach you to quickly realize the transfer of high-quality artistic style Can roam the universe without a spaceship?! 👨‍🚀👩‍🚀 You're not dreaming! 🚀 AI art style migration makes your dream come true!!! 🛰 Don't feel out of reach today PaddleGAN Just use the power of AI to lead you on th ...

Preface I am following the monthly release of 2021.09.28: v2.17.0 This article explains the following: mmDetection Training Core ComponentsmmDetection Test Core ComponentsBuild a faster rcnn configuration file using the core components above There is no specific code implementation involved here, but familiarize yourself with the main c ...

ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation Paszke, A., Chaurasia, A., Kim, S., & Culurciello, E. (2016). ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation. ArXiv, abs/1606.02147. # Initial block of the model: # Input # / \ # / \ #maxpoo ...

The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation In this paper, DenseNets is extended to solve the problem of semantic segmentation. The best results were obtained on the urban scene benchmark data sets (CamVid and Gatech), without further post-processing modules (such as CRF) and pre training models. In ...

catalogue 1. Basic principle of Zhang's calibration method 1.1 camera pinhole model 1.2 homography matrix H 1.3 internal parameters of camera 1.4 external parameters of camera   1.5 optimization parameters 1.6 summary 2.OpenCV implementation 2.1 feature point detection and target coordinate initialization 2.2 camera calibration ...

openCV Window, mouse and keyboard operation Comprehensive example Generate a 500 * 500 pure black canvas, create a window, add mouse events to the window, and use the window to display images import cv2 import numpy as np # Generate 500 * 500 pure black canvas convas = np.zeros(shape=(512, 512, 3), dtype=np.uint8) # crea ...

Deep learning image classification (XVIII) detailed explanation of Vision Transformer(ViT) network In the previous section, we talked about the self attention structure in Transformer. In this section, learn the detailed explanation of Vision Transformer(vit). Learning video from Bilibili , refer to blog Detailed explanation of Vision Tran ...

Summary: SLIC: simple linear iterative clustering, a simple linear iterative clustering, uses k-means clustering to generate super-pixels, which is simple to implement, efficient to run, and has a better boundary preservation effect. The specific results compared with other methods are detailed in the following paper. Paper address:https: ...