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/ben0110/Frustum_pointnet_2D

Frustum PointNets for 3D Object Detection from RGB-D Data

https://github.com/ben0110/Radar-Pointnet-PARA

Frustum PointNets for 3D Object Detection from RGB-D Data

https://github.com/ben0110/frustum-pointnets_RSC_RADAR_fil_PC_batch_para

Frustum PointNets for 3D Object Detection from RGB-D Data

https://github.com/benihime91/pytorch_retinanet

Feature Pyramid Networks for Object Detection

https://github.com/benjaminrwilson/inequity-release

Predictive Inequity in Object Detection

https://github.com/benjamintli/darknet-gun-detector

YOLO9000: Better, Faster, Stronger

https://github.com/benjamintli/training-results-azure

YOLO9000: Better, Faster, Stronger

https://github.com/bennyqp/artificial-inspiration

Going Deeper with Convolutions

https://github.com/bentrevett/pytorch-image-classification

Deep Residual Learning for Image Recognition

https://github.com/benywon/ChiQA

End-to-End Object Detection with Transformers

https://github.com/berenslab/retinal-vessel-segmentation-benchmark

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/beresandras/contrastive-classification-keras

Barlow Twins: Self-Supervised Learning via Redundancy Reduction

https://github.com/berkandemirel/zero-shot-detection

Zero-Shot Object Detection by Hybrid Region Embedding

https://github.com/berniwal/swin-transformer-pytorch

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/bertrandlalo/deep_dog_breeds

Deep Residual Learning for Image Recognition

https://github.com/bespontaneous/ffn

TSM: Temporal Shift Module for Efficient Video Understanding

https://github.com/bespontaneous/mca-pytorch

TSM: Temporal Shift Module for Efficient Video Understanding

https://github.com/bespontaneous/proteus-pytorch

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/bethgelab/InDomainGeneralizationBenchmark

Deep Residual Learning for Image Recognition

https://github.com/bethgelab/cifar10_challenge

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/bfortuner/pytorch_tiramisu

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/bharath3794/UNet-Image-Segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/bharatsingh430/soft-nms

Soft-NMS -- Improving Object Detection With One Line of Code

https://github.com/bharatsush/object_detection

YOLO9000: Better, Faster, Stronger

https://github.com/bhfs9999/maskrcnn_isic

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

https://github.com/bhneo/DecorrelatedBN_tf

Iterative Normalization: Beyond Standardization towards Efficient Whitening

https://github.com/bhneo/decorrelated_bn

Iterative Normalization: Beyond Standardization towards Efficient Whitening

https://github.com/bhumigodiwala/Face-Mask-Detection

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/bhuyanamit986/FlowerClassification

Going Deeper with Convolutions

https://github.com/bhyun-kim/NWD

A Normalized Gaussian Wasserstein Distance for Tiny Object Detection

https://github.com/bian0505/Pad_Me

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

https://github.com/bigmb/Unet-Segmentation-Pytorch-Nest-of-Unets

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/billlyzhaoyh/SegNetFromScratch

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

https://github.com/bimal1988/detector

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

https://github.com/bingcheng45/hnr-extension

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/binhdv92/darknet

YOLO9000: Better, Faster, Stronger

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