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by Hengshuang Zhao\, Yi Zhang\, Shu Liu, Jianping Shi, Chen Change Loy, Dahua Lin, Jiaya Jia, details are in project page.
Learning to Adapt Structured Output Space for Semantic Segmentation, CVPR 2018 (spotlight)
ICNet for Real-Time Semantic Segmentation on High-Resolution Images, ECCV2018
This project is a computer vision benchmark and image classification dataset used to measure and compare the accuracy of machine learning models. It provides a standardized collection of labeled fashion product images and training data formatted to be compatible with the MNIST dataset structure. The dataset consists of fixed-dimension grayscale images and label-based category mappings, stored in a binary format. It includes pre-split training and testing sets and a static distribution to ensure consistent cross-model benchmarking. The repository supports image classification benchmarking and
ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation
The main features of sacmehta/espnet are: Computer Vision Research, Segmentation Architectures.
Projects with overlapping indexed features include: wasidennis/adaptsegnet — Learning to Adapt Structured Output Space for Semantic Segmentation, CVPR 2018 (spotlight). hszhao/psanet — by Hengshuang Zhao\, Yi Zhang\, Shu Liu, Jianping Shi, Chen Change Loy, Dahua Lin, Jiaya Jia, details are in project… hszhao/icnet — ICNet for Real-Time Semantic Segmentation on High-Resolution Images, ECCV2018. zalandoresearch/fashion-mnist — This project is a computer vision benchmark and image classification dataset used to measure and compare the accuracy… agrimgupta92/sgan — Code for "Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks", Gupta et al, CVPR 2018. 4uiiurz1/pytorch-nested-unet — PyTorch implementation of UNet++ (Nested U-Net).