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/saimj7/People-Counting-in-Real-Time

SSD: Single Shot MultiBox Detector

https://github.com/saimj7/Social-Distancing-Detection-in-Real-Time

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

https://github.com/sair-lab/ni-slam

Kernel Cross-Correlator

https://github.com/sajjjadayobi/CLIPfa

Learning Transferable Visual Models From Natural Language Supervision

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

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/sakurusurya2000/CNN

Semantic Perceptual Image Compression using Deep Convolution Networks

https://github.com/salem-devloper/COVID-Lung-Segment

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/salem-devloper/final

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/salem-devloper/lung-chaker

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/salesforce/ALBEF

Align before Fuse: Vision and Language Representation Learning with Momentum Distillation

https://github.com/salesforce/blip

BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation

https://github.com/salimkhazem/U-net-Segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/salman-ghauri/keras_frcnn

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

https://github.com/salman-h-khan/PL-ZSD_Release

Polarity Loss for Zero-shot Object Detection

https://github.com/salman-h-khan/ZSD_Release

Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts

https://github.com/salomonhotegni/MOREL

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/saloni1998/Cifar-10

Deep Residual Learning for Image Recognition

https://github.com/samirgholipour/speed_estimation

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/sammyamajumdar/ResNet50

Deep Residual Learning for Image Recognition

https://github.com/samo1petar/SSD

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

https://github.com/samplecm/Organogenesis

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/samsh19/ML_project

Mask R-CNN

https://github.com/samson6460/tf2_Segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/samson6460/tf2_YOLO

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

https://github.com/samson6460/tf2_pose_estimation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/samsonadmin/modified-alexeyab-darknet

YOLO9000: Better, Faster, Stronger

https://github.com/sananand007/Xrelab

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

https://github.com/sandeeplbscek/awesome-deeplearning-resources

Rich feature hierarchies for accurate object detection and semantic segmentation

https://github.com/sandeepsinghsengar/Pytoarch-UNet

U-Net: Convolutional Networks for Biomedical Image Segmentation

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

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/sandipsahajoy/Predicting-Lymphoma-using-CNN-in-Keras

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/sangHa0411/CIFAR100

Deep Residual Learning for Image Recognition

https://github.com/sangHa0411/ResNetCIFAR100

Deep Residual Learning for Image Recognition

https://github.com/sangamdeuja/Helsinki_pedestrian_crossing_detection

Rich feature hierarchies for accurate object detection and semantic segmentation

https://github.com/sanghyun-son/clustering-kernels

Deep Residual Learning for Image Recognition

https://github.com/sanjaykrishnanrs/Project-7

Going Deeper with Convolutions

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