Papers with Code 아카이브

Object Detection

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

39

평가표

124

보존된 평가 기록

977

코드

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

https://github.com/busyboxs/What-I-have-star

Mask R-CNN

https://github.com/busyboxs/faster_rcnn_voc

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

https://github.com/buyeah1109/KEN

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/buyeah1109/finc

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/bwconrad/masked-autoencoder

Masked Autoencoders Are Scalable Vision Learners

https://github.com/byahn2/clevr_ref

CLEVR-Ref+: Diagnosing Visual Reasoning with Referring Expressions

https://github.com/bytedance/next-vit

Beyond Self-Supervision: A Simple Yet Effective Network Distillation Alternative to Improve Backbones

https://github.com/c1ph3rr/Deep-Residual-Learning-for-Image-Recognition

Deep Residual Learning for Image Recognition

https://github.com/c1ph3rr/U-Net-Convolutional-Networks-For-Biomedicalimage-Segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/cabeen/neu-net

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/cai199626/PDNet

PDNet: Prior-model Guided Depth-enhanced Network for Salient Object Detection

https://github.com/caihaocong/Yolov3_AlexeyAB_Version

YOLO9000: Better, Faster, Stronger

https://github.com/caihuaiguang/CHG-Shapley-for-Data-Selection

Learning From Less Data: A Unified Data Subset Selection and Active Learning Framework for Computer Vision

https://github.com/calmisential/TensorFlow2.0_ResNet

Deep Residual Learning for Image Recognition

https://github.com/calmisential/YOLOv3_TensorFlow2

YOLOv3: An Incremental Improvement

https://github.com/camenduru/grounded-segment-anything-colab

Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection

https://github.com/cameronchoi/r3det-copy

Deep Residual Learning for Image Recognition

https://github.com/cameronthurston/CSAILVisionUpdated

Acquisition of Localization Confidence for Accurate Object Detection

https://github.com/camma-public/ssg-qa

EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos

https://github.com/camma-public/ssg-vqa

EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos

https://github.com/camma-public/tf-cholec80

EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos

https://github.com/canerozer/qct

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/canturan10/satellighte

Deep Residual Learning for Image Recognition

https://github.com/caojoshua/Plastic-Recyclability

Deep Residual Learning for Image Recognition

https://github.com/caoquan95/6D-pose-project

DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion

https://github.com/capstone-w3/trash_parent_repo

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

https://github.com/captaindario/dakanji-single-kanji-recognition

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/cardwing/codes-for-pvkd

PolarMix: A General Data Augmentation Technique for LiDAR Point Clouds

https://github.com/carinanorre/Brain-Tumour-Segmentation-Dissertation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/carolchenyx/MobilenetXT-tensorflow

Rethinking Bottleneck Structure for Efficient Mobile Network Design

https://github.com/carterprice2/Deep_Learning_project

PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud

https://github.com/casiopa/Madrid_Rooftops

Mask R-CNN

https://github.com/castorini/deebert

Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges

https://github.com/castorini/earlyexiting-monobert

Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges

https://github.com/catalpaaa/demansia

Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model

https://github.com/cattidea/VinVL-Paddle

VinVL: Revisiting Visual Representations in Vision-Language Models

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