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carolynxzy's Projects

bcnet icon bcnet

Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers [CVPR 2021]

clip icon clip

Contrastive Language-Image Pretraining

datasets icon datasets

🎁 3,000,000+ Unsplash images made available for research and machine learning

hrnet-semantic-segmentation icon hrnet-semantic-segmentation

The OCR approach is rephrased as Segmentation Transformer: https://arxiv.org/abs/1909.11065. This is an official implementation of semantic segmentation for HRNet. https://arxiv.org/abs/1908.07919

kubernetes icon kubernetes

Production-Grade Container Scheduling and Management

lama icon lama

🦙 LaMa Image Inpainting, Resolution-robust Large Mask Inpainting with Fourier Convolutions, WACV 2022

mae icon mae

PyTorch implementation of MAE https//arxiv.org/abs/2111.06377

moby icon moby

Moby Project - a collaborative project for the container ecosystem to assemble container-based systems

pen-net-for-inpainting icon pen-net-for-inpainting

[CVPR'2019] PEN-Net: Learning Pyramid-Context Encoder Network for High-Quality Image Inpainting

pi-temp icon pi-temp

Web server using a Raspberry Pi and DHT22 sensor to graph the humidity and temperature in my apartment over time.

pyqt icon pyqt

使用PyQt实现的项目

pytorch-fcn icon pytorch-fcn

PyTorch Implementation of Fully Convolutional Networks. (Training code to reproduce the original result is available.)

pytorch-unet icon pytorch-unet

PyTorch implementation of the U-Net for image semantic segmentation with high quality images

springboot-learning-example icon springboot-learning-example

spring boot 实践学习案例,是 spring boot 初学者及核心技术巩固的最佳实践。另外写博客,用 OpenWrite。

spyne icon spyne

A transport and architecture agnostic RPC library that focuses on exposing services with a well-defined API using popular protocols.

uoais icon uoais

Codes of paper "Unseen Object Amodal Instance Segmentation via Hierarchical Occlusion Modeling", ICRA 2022

vision icon vision

Datasets, Transforms and Models specific to Computer Vision

walt icon walt

[CVPR 2022] WALT: Watch And Learn 2D amodal representation from Time-lapse imagery

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