Papers with Code 아카이브

2D Object Detection

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

15

평가표

20

보존된 평가 기록

57

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/chakerouari/UNET_segmetation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/chandra411/Product-Detection

SSD: Single Shot MultiBox Detector

https://github.com/chandrikadeb7/face-mask-detection

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/changliu816/CV-paper-review

Non-local Neural Networks

https://github.com/chanlilong/4D_NET_pytorch

4D-Net for Learned Multi-Modal Alignment

https://github.com/chaowang1994/caffe-ssd

SSD: Single Shot MultiBox Detector

https://github.com/charlesCXK/RGBD_Semantic_Segmentation_PyTorch

Bi-directional Cross-Modality Feature Propagation with Separation-and-Aggregation Gate for RGB-D Semantic Segmentation

https://github.com/charlesYangM/py-faster-rcnn-80.28

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

https://github.com/charlescxk/depth2hha-python

Learning Rich Features from RGB-D Images for Object Detection and Segmentation

https://github.com/charlesq34/pointnet

Frustum PointNets for 3D Object Detection from RGB-D Data

https://github.com/charlesq34/pointnet2

Frustum PointNets for 3D Object Detection from RGB-D Data

https://github.com/charlesshang/fastmaskrcnn

Mask R-CNN

https://github.com/charlychiu/U-Net

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/chartgalaxy/chartgalaxy

OrionBench: A Benchmark for Chart and Human-Recognizable Object Detection in Infographics

https://github.com/chayanchatterjee/cbc-skynet

Deep Residual Learning for Image Recognition

https://github.com/chayanchatterjee/gw-skylocator

Deep Residual Learning for Image Recognition

https://github.com/cheems-seminar/grounded-segment-any-parts

Going Denser with Open-Vocabulary Part Segmentation

https://github.com/chen-si-jia/Trajectory-Long-tail-Distribution-for-MOT

FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking

https://github.com/chenbys/GuidedOffset

R-FCN: Object Detection via Region-based Fully Convolutional Networks

https://github.com/chencq1234/maskrcnn_facebook

RetinaMask: Learning to predict masks improves state-of-the-art single-shot detection for free

https://github.com/chenfengxu714/image2point

Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution

https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch

Deformable ConvNets v2: More Deformable, Better Results

https://github.com/chengguixiong1010/rnn_STAF_2dpose

Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields

https://github.com/chenghan111/dnc

SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

https://github.com/chenghuaiyu/caffe

R-FCN: Object Detection via Region-based Fully Convolutional Networks

https://github.com/chengpengchen/repghost

MobileOne: An Improved One millisecond Mobile Backbone

https://github.com/chengtan9907/simvpv2

Visual Attention Network

https://github.com/chengyangfu/pytorch-groupnormalization

Group Normalization

https://github.com/chengzhengxin/groupsoftmax-simpledet

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

https://github.com/chenjian123456/caffe-ssd

SSD: Single Shot MultiBox Detector

https://github.com/chenjunweii/mxnet-ssd

SSD: Single Shot MultiBox Detector

https://github.com/chenller/mmseg-extension

Vision Transformer Adapter for Dense Predictions

https://github.com/chenmengyang/rename_later

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/chenquan-cq/PFPN

Progressive Feature Polishing Network for Salient Object Detection

https://github.com/chensnathan/YOLOF

You Only Look One-level Feature

https://github.com/chenwuperth/ClaRAN

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.