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/haratzouvara/image_restoration

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/harinath0906/Object-Detection-using-PyTorch

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

https://github.com/harinath0906/Wheat-Head-Detection-using-PyTorch

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

https://github.com/harishhirthi/Indian-Traffic-Segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/harrybolingot/mymaskrcnn

Mask R-CNN

https://github.com/harsh-jadhav/SIIM-Pneumothorax-Case-Study2

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/harsh2011/Yolov3-Detector

YOLOv3: An Incremental Improvement

https://github.com/harsh2912/people-tracking

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

https://github.com/harshalsonioo1/hpe

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

https://github.com/harshit0511/Deep-Learning

Squeeze-and-Excitation Networks

https://github.com/harshm121/m3l

Multimodal Token Fusion for Vision Transformers

https://github.com/hasanirtiza/PedesFormer-SwinTransformer-For-Pedestrian-Detection

Pedestrian Detection: Domain Generalization, CNNs, Transformers and Beyond

https://github.com/hatimwen/paddle_pit

Rethinking Spatial Dimensions of Vision Transformers

https://github.com/hattrickcr7/SoftTeacher

A Simple Semi-Supervised Learning Framework for Object Detection

https://github.com/hayashimasa/UNet-PyTorch

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/hazyresearch/domino

Where Does My Model Underperform? A Human Evaluation of Slice Discovery Algorithms

https://github.com/hbwang1427/image_retrieval

Faster R-CNN Features for Instance Search

https://github.com/hbzhang/AwesomeSelfDriving

Dual Attention Network for Scene Segmentation

https://github.com/hchoi256/i-halla-v1.0

Open-Vocabulary DETR with Conditional Matching

https://github.com/hdjang/Feature-Selective-Anchor-Free-Module-for-Single-Shot-Object-Detection

Feature Selective Anchor-Free Module for Single-Shot Object Detection

https://github.com/hehehemin/DotaDetection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/heidongxianhau/deeplab2

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/hellochick/ICNet-tensorflow

ICNet for Real-Time Semantic Segmentation on High-Resolution Images

https://github.com/hellohaozheng/maskrcnn-mindspore

Mask R-CNN

https://github.com/hellotrik/torch_yolov3

YOLOv3: An Incremental Improvement

https://github.com/helq2612/biadt

DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR

https://github.com/hemanth346/mde_bs

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/henbucuoshanghai/sparsercnn

Sparse R-CNN: End-to-End Object Detection with Learnable Proposals

https://github.com/henry8527/GCE

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/hershd23/ObjectLocalizer

Deep Residual Learning for Image Recognition

https://github.com/hexhowells/Neural-Network-Implementations/tree/main/models/ParNet

Non-deep Networks

https://github.com/hey-yahei/CondConv.MXNet

CondConv: Conditionally Parameterized Convolutions for Efficient Inference

https://github.com/hggym/pigo

Object Detection with Pixel Intensity Comparisons Organized in Decision Trees

https://github.com/hh-xiaohu/Image-augementation-pytorch

Training Region-based Object Detectors with Online Hard Example Mining

https://github.com/hhaAndroid/u2net

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

https://github.com/hhk7734/tensorflow-yolov4

YOLOv4: Optimal Speed and Accuracy of Object Detection

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