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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.
The main features of hszhao/psanet 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). sacmehta/espnet — ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation. 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).
ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation
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