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/adam-mehdi/MuarAugment

RandAugment: Practical automated data augmentation with a reduced search space

https://github.com/adam123wu/edge-yolo

YOLOv3: An Incremental Improvement

https://github.com/adamdad/dery

Deep Incubation: Training Large Models by Divide-and-Conquering

https://github.com/adamlakatos0407/ScaledYOLOv4

Scaled-YOLOv4: Scaling Cross Stage Partial Network

https://github.com/adamvest/keypoint-baseline

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

https://github.com/addisonklinke/pytorch-architectures/blob/377548954f13d32e45e5641396e3839a9103395b/torcharch/conv.py

Feature Pyramid Networks for Object Detection

https://github.com/addisonklinke/pytorch-architectures/blob/master/torcharch/conv.py

Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

https://github.com/adeelz92/DaD-Framework

Detect-and-describe: Joint learning framework for detection and description of objects

https://github.com/adept-thu/SATGCN

S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection

https://github.com/aditya30394/Person-Re-Identification

Random Erasing Data Augmentation

https://github.com/adityaarun1/pytorch_fast-er_rcnn

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

https://github.com/adityajn105/brain-tumor-segmentation-unet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/adityarajsahu/UNet-Implementation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/adldotori/ResNet

Deep Residual Learning for Image Recognition

https://github.com/adrienangeli/dino

Emerging Properties in Self-Supervised Vision Transformers

https://github.com/adursun/wsddn.pytorch

Weakly Supervised Deep Detection Networks

https://github.com/afzalahmad0203/Numpy-Shufflenet

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

https://github.com/afzalahmad0203/Tensorflow-Shufflenet

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

https://github.com/afzalahmad0203/tf_shufflenet

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

https://github.com/agelencs/darknet_original

YOLOv3: An Incremental Improvement

https://github.com/agutuyen-dev/darknet

YOLO9000: Better, Faster, Stronger

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

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/ahhan02/darknet-alex

YOLO9000: Better, Faster, Stronger

https://github.com/ahmed-anas/thesis-pointnet

VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection

https://github.com/ahmedelmahy/myownvit

Rethinking Spatial Dimensions of Vision Transformers

https://github.com/ahtwq/SENet

Squeeze-and-Excitation Networks

https://github.com/ai-forever/ru-clip

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/ai-med/squeeze_and_excitation

Squeeze-and-Excitation Networks

https://github.com/ai4ce/FLAT

Fooling LiDAR Perception via Adversarial Trajectory Perturbation

https://github.com/aibeedetect/bfjdet

CrowdHuman: A Benchmark for Detecting Human in a Crowd

https://github.com/aiha-lab/AI-thermometer

SSD: Single Shot MultiBox Detector

https://github.com/aiiu-lab/naturalistic-adversarial-patch

Re-labeling ImageNet: from Single to Multi-Labels, from Global to Localized Labels

https://github.com/aim-uofa/AdelaiDet

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/aim-uofa/adet

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/aimerykong/RGBT-detection

Multimodal Object Detection via Probabilistic Ensembling

https://github.com/aimerykong/predictive-filter-flow

Recurrent Pixel Embedding for Instance Grouping

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