Papers with Code 아카이브

Object Detection

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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하위 태스크

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평가표

124

보존된 평가 기록

977

코드

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

https://github.com/mathilde-b/SFDA

Common Limitations of Image Processing Metrics: A Picture Story

https://github.com/mathildor/DeepLab-v3

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/matsui528/manga109api

Object Detection for Comics using Manga109 Annotations

https://github.com/mattangus/fast-semantic-segmentation

Pyramid Scene Parsing Network

https://github.com/mattdeitke/cvpr-buzz

VirTex: Learning Visual Representations from Textual Annotations

https://github.com/mattdutson/mobilenet

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/maudzung/CenterNet3D-PyTorch

Objects as Points

https://github.com/maudzung/Complex-YOLOv4-Pytorch

Complex-YOLO: Real-time 3D Object Detection on Point Clouds

https://github.com/maudzung/RTM3D

Objects as Points

https://github.com/maudzung/SFA3D

Objects as Points

https://github.com/maudzung/TTNet-Realtime-for-Table-Tennis-Pytorch

TTNet: Real-time temporal and spatial video analysis of table tennis

https://github.com/maudzung/YOLO3D-YOLOv4-PyTorch

YOLO3D: End-to-end real-time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloud

https://github.com/maudzung/super-fast-accurate-3d-object-detection

Objects as Points

https://github.com/maxfrei750/DeepParticleNet

Mask R-CNN

https://github.com/maxfrei750/FibeR-CNN

Mask R-CNN

https://github.com/maxpark/SkyNet-1

FPGA/DNN Co-Design: An Efficient Design Methodology for IoT Intelligence on the Edge

https://github.com/maxsenh/nuclei_cell_detect

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

https://github.com/maxwelltsai/DeepGalaxy

Deep Residual Learning for Image Recognition

https://github.com/mayankagarwal44442/Object-Detection-and-localization

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

https://github.com/mayanks888/yolo_custom

YOLOv3: An Incremental Improvement

https://github.com/mazabou/darknet

YOLOv3: An Incremental Improvement

https://github.com/mberkay0/pretrained-backbones-unet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/mbsariyildiz/resnet-pytorch

Deep Residual Learning for Image Recognition

https://github.com/mbuet2ner/local-global-features-cnn

ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness

https://github.com/mczhuge/Kaleido-BERT

Salient Object Detection via Integrity Learning

https://github.com/mczhuge/SOCToolbox

Salient Object Detection via Integrity Learning

https://github.com/mdda/deep-learning-models

Going Deeper with Convolutions

https://github.com/mdt48/semantic-segmentation-pytorch

Pyramid Scene Parsing Network

https://github.com/mdt48/yolov3-HotelsObjectDetection

YOLOv3: An Incremental Improvement

https://github.com/mdv3101/darknet-yolov3

YOLO9000: Better, Faster, Stronger

https://github.com/meanderingstream/semi_supervised

Self-Supervised Learning of Pretext-Invariant Representations

https://github.com/mediteamC/teamC

VoxResNet: Deep Voxelwise Residual Networks for Volumetric Brain Segmentation

https://github.com/meerkatai/Yolo-v4-Object-Detection-Classification

YOLOv3: An Incremental Improvement

https://github.com/mees/deep_adaptive_fusion

Choosing Smartly: Adaptive Multimodal Fusion for Object Detection in Changing Environments

https://github.com/megapose6d/megapose6d

MegaPose: 6D Pose Estimation of Novel Objects via Render & Compare

https://github.com/megvii-model/ShuffleNet-Series/tree/master/ShuffleNetV2

ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design

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