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/chenxd2/Detecting-Cancer-Metastases-on-Gigapixel-Pathology-Images

Detecting Cancer Metastases on Gigapixel Pathology Images

https://github.com/chenxi116/DeepLabv3.pytorch

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/chenxy99/AttFDNet

Leveraging Bottom-Up and Top-Down Attention for Few-Shot Object Detection

https://github.com/chenyang4/resmooth

RandAugment: Practical automated data augmentation with a reduced search space

https://github.com/chenyicai-0611/YOLOX-Flask-deployment

YOLOX: Exceeding YOLO Series in 2021

https://github.com/chenyilun95/tf-cpn

MegDet: A Large Mini-Batch Object Detector

https://github.com/chenyizi086/wu.2023.sigspatial

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/chenyouxin113/sota-status-investigation

Learning Data Augmentation Strategies for Object Detection

https://github.com/chenyuntc/simple-faster-rcnn-pytorch

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

https://github.com/cheonlian/DeepfakeBlock

Transferable Adversarial Attacks for Image and Video Object Detection

https://github.com/chequanghuy/TriLiteNet

https://github.com/chetanmreddy/voxelnet_chetan

ImVoxelNet: Image to Voxels Projection for Monocular and Multi-View General-Purpose 3D Object Detection

https://github.com/chihyanghsu0805/object_detection_yolo

You Only Look Once: Unified, Real-Time Object Detection

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

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/chincharles/u-emo

YOLOv3: An Incremental Improvement

https://github.com/chinhsuanwu/mobilevit-pytorch

MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer

https://github.com/chiragsamal/Faster-R-CNN-Object-detection

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

https://github.com/chjort/u2net

U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection

https://github.com/chonepieceyb/reading-frustum-pointnets-code

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

https://github.com/chongruo/detectron2-resnest

ResNeSt: Split-Attention Networks

https://github.com/chongweiliu/DUT-USEG

An Underwater Image Semantic Segmentation Method Focusing on Boundaries and a Real Underwater Scene Semantic Segmentation Dataset

https://github.com/chonyy/AI-basketball-analysis

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/chonyy/ML-auto-baseball-pitching-overlay

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/chouxianyu/Boundary-Aware-PoolNet

A Simple Pooling-Based Design for Real-Time Salient Object Detection

https://github.com/chowkamlee81/CentrePointNet

Center-based 3D Object Detection and Tracking

https://github.com/chrhenning/hypercl

Deep Residual Learning for Image Recognition

https://github.com/chris-boson/fashion_mnist

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/chrisqqq123/FA-Dist-EfficientNet

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/christiansafka/img2vec

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/christophmeyer/longboard-pothole-detection

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/chrisway613/MixConv

MixConv: Mixed Depthwise Convolutional Kernels

https://github.com/chrisway613/Synchronized-BatchNormalization

MegDet: A Large Mini-Batch Object Detector

https://github.com/chuanqichen/deepcoaching

Mask R-CNN

https://github.com/chuongngd/Images-Object-Detection

You Only Look Once: Unified, Real-Time Object Detection

https://github.com/chwilms/sos

Learning Open-World Object Proposals without Learning to Classify

https://github.com/cihangxie/DAG

Adversarial Examples for Semantic Segmentation and Object Detection

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