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/arpytanshu/ssd-object-detection

SSD: Single Shot MultiBox Detector

https://github.com/arraiyopensource/torchgeometry

Kornia: an Open Source Differentiable Computer Vision Library for PyTorch

https://github.com/arsalhuda24/SS_lstm

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

https://github.com/arsenal9971/shearlet_semantic_edge

CASENet: Deep Category-Aware Semantic Edge Detection

https://github.com/artemmavrin/focal-loss

Focal Loss for Dense Object Detection

https://github.com/arthurflor23/surface-crack-detection

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/artxtech/darknet-rnn

YOLO9000: Better, Faster, Stronger

https://github.com/artynet/darknet-alexeyAB

YOLO9000: Better, Faster, Stronger

https://github.com/arvind1998/SSD-Object-Detection

SSD: Single Shot MultiBox Detector

https://github.com/aryanasadianuoit/MobileNet_V2

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/aryanasadianuoit/mobilenet_v1

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/asad-62/IVP-DNN

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/aschethor/Spline_PINN

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/aschethor/Unsupervised_Deep_Learning_of_Incompressible_Fluid_Dynamics

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/asharakeh/kitti_native_evaluation

Joint 3D Proposal Generation and Object Detection from View Aggregation

https://github.com/ashishpapanai/uNet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/ashishpatel26/BrainMRI-Segmentation-Keras

Deep Residual Learning for Image Recognition

https://github.com/ashishsalunkhe/Understanding-RCNN-Fast-RCNN-Faster-RCNN-and-Mask-RCNN

Rich feature hierarchies for accurate object detection and semantic segmentation

https://github.com/ashley-kim521/11785-project-active-learning-for-YOLO

Learning Loss for Active Learning

https://github.com/ashwath007/amenity-detection

SSD: Single Shot MultiBox Detector

https://github.com/ashwath007/aminity-detection

SSD: Single Shot MultiBox Detector

https://github.com/asprenger/keras_fc_densenet

Deep Residual Learning for Image Recognition

https://github.com/asrafulashiq/dynamic-cdfsl

A Broad Study on the Transferability of Visual Representations with Contrastive Learning

https://github.com/assiaben/hed

Holistically-Nested Edge Detection

https://github.com/astaxanthin/adasp

TOOD: Task-aligned One-stage Object Detection

https://github.com/astoycos/Mini_Project2

Deep Residual Learning for Image Recognition

https://github.com/asultan123/DL_LLC_Prefetching

LambdaNetworks: Modeling Long-Range Interactions Without Attention

https://github.com/asvath/mobile_robotics

nuScenes: A multimodal dataset for autonomous driving

https://github.com/asylve/Sea-Ice

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/asyrovprog/cs230project

Mask R-CNN

https://github.com/atharva417/Yoga_Pose_GNR638

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/atregret/mobilenetv3

Searching for MobileNetV3

https://github.com/au1206/Enhance-GAN

DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks

https://github.com/aubricot/object_detection_for_image_cropping

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

https://github.com/augcog/dttdv1

Robust 6DoF Pose Estimation Against Depth Noise and a Comprehensive Evaluation on a Mobile Dataset

https://github.com/aurelien-peden/Deep-Learning-paper-implementations

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

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