陈林蔚's repositories

LIS

IJCV2023 Instance Segmentation in the Dark

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FADC

CVPR 2024 Highlight: Frequency-Adaptive Dilated Convolution for Semantic Segmentation

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Deep-Residual-Network-for-JointDemosaicing-and-Super-Resolution

Deep-Residual-Network-for-JointDemosaicing-and-Super-Resolution pytorch 复现工作

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MTCNN_Pytorch

MTCNN Pytorch implementation

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Seg-Aliasing

ICLR2024 When Sementic Segmentation Meets Frequency Aliasing

YOLOv1-Pytorch

YOLOv1 implementation of Pytorch with multi-backbone

FCN-Pytorch

Pytorch implement of FCN32/16/8s

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CASID

CASID: A Large-scale Climate-Aware Satellite Image Dataset for Domain Adaptive Land-Cover Semantic Segmentation

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Faster_R-CNN-Pytorch

simple implementation of Faster R-CNN with Pytorch

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Pytorch-Implement-of-Papers

Pytorch-Implementation-of-Papers

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MLM

A large scale high quality dataset called Multi-Level Map (MLM) for map generation at multiple zoom levels

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ResNeXt-Pytorch

Pytorch implement of ResNeXt

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SSD300-VGG16-Pytorch

SSD300-VGG16-Pytorch-Implementation

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easy-faster-rcnn.pytorch

An easy implementation of Faster R-CNN (https://arxiv.org/pdf/1506.01497.pdf) in PyTorch.

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fade

FADE: Fusing the Assets of Decoder and Encoder for Task-Agnostic Upsampling

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faster-rcnn.pytorch

A faster pytorch implementation of faster r-cnn

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LODDataset

This is Low-light Object Detection (LOD) dataset of Crafting Object Detection in Very Low Light in BMVC(British Machine Vision Conference) 2021, by Yang Hong, Kaixuan Wei, Linwei Chen, and Ying Fu.

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numpy-100

100 numpy exercises (100% complete)

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pretrained-models.pytorch

Pretrained ConvNets for pytorch: NASNet, ResNeXt, ResNet, InceptionV4, InceptionResnetV2, Xception, DPN, etc.

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pytorch-gpu-benchmark

Using the famous cnn model in Pytorch, we run benchmarks on various gpu.

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pytorch-YOLO-v1

an experiment for yolo-v1, including training and testing.

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pytorch-yolo-v3

A PyTorch implementation of the YOLO v3 object detection algorithm

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PyTorch-YOLOv3

Minimal PyTorch implementation of YOLOv3

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up

Official code repository for the paper "Unite the People – Closing the Loop Between 3D and 2D Human Representations".

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