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/amazon-research/mix-generation

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

https://github.com/amazon-research/stochastic-backpropagation

An In-depth Study of Stochastic Backpropagation

https://github.com/amazon-science/bigdetection

A Simple Semi-Supervised Learning Framework for Object Detection

https://github.com/amazon-science/long-short-term-transformer

Deep Residual Learning for Image Recognition

https://github.com/amdegroot/ssd.pytorch

SSD: Single Shot MultiBox Detector

https://github.com/amemetov/ssd

SSD: Single Shot MultiBox Detector

https://github.com/amerch/CIFAR100-Training

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/aminekha/AI-For-2022

YOLO9000: Better, Faster, Stronger

https://github.com/amingwu/single-dgod

https://github.com/amirassov/kaggle-imaterialist

Hybrid Task Cascade for Instance Segmentation

https://github.com/amirbar/FilterResponseNormalization

Filter Response Normalization Layer: Eliminating Batch Dependence in the Training of Deep Neural Networks

https://github.com/amirdy/dog-breed-classification

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/amirfaraji/LowDoseCTPytorch

UNet++: A Nested U-Net Architecture for Medical Image Segmentation

https://github.com/amirgholami/pyhessian

HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks

https://github.com/amirmohammadii/SSD-object-detection

SSD: Single Shot MultiBox Detector

https://github.com/amitgupta223/Real-Time-Face-Mask-Detection-using-OpenCV-on-MacOS

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/amittiwary42/target-detection-eeg-signals

Focal Loss for Dense Object Detection

https://github.com/amogh7joshi/engagement-detection

Deep Residual Learning for Image Recognition

https://github.com/amogh7joshi/fer

Deep Residual Learning for Image Recognition

https://github.com/amogh7joshi/plant-health-detection

Deep Residual Learning for Image Recognition

https://github.com/amol-matkar/Face-Mask-Segmentation

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/amritasaha1812/pytorch-faster-rcnn

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

https://github.com/amshaker/swiftformer

EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision Applications

https://github.com/anandjebakumar/unet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/anant95/K_means-image-compression

Semantic Perceptual Image Compression using Deep Convolution Networks

https://github.com/ananyahjha93/swav

End-to-End Object Detection with Transformers

https://github.com/anastasiia-kornilova/MMDF

DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks

https://github.com/anatolix/keras_Realtime_Multi-Person_Pose_Estimation

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

https://github.com/andersonstra/mukea

OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge

https://github.com/andreasveit/densenet-pytorch

Deep Residual Learning for Image Recognition

https://github.com/andrewilyas/ens-adv-train-attack

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/andrewowens/multisensory

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/andrey1908/mmdetection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/andrijdavid/clinical-brain-interface-2020

Deep Residual Learning for Image Recognition

https://github.com/andycao1125/mambadm

Chasing Day and Night: Towards Robust and Efficient All-Day Object Detection Guided by an Event Camera

https://github.com/andyzeng/apc-vision-toolbox

Fully Convolutional Networks for Semantic Segmentation

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