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/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/anenbergb/Co-DETR-TensorRT

DETRs with Collaborative Hybrid Assignments Training

https://github.com/anfidthtn/HAC

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

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