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/makefile/frcnn

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

https://github.com/malin9402/2020-0221

Deep Residual Learning for Image Recognition

https://github.com/manankshastri/Object-Detection

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

https://github.com/manantomar/video-occupancy-models

Emerging Properties in Self-Supervised Vision Transformers

https://github.com/mangye16/Unsupervised_Embedding_Learning

Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination

https://github.com/manideep2510/eye-in-the-sky

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/manikandan-ravikiran/squeezeDet

SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving

https://github.com/manishravula/yolov3_tiny_pruned

YOLO9000: Better, Faster, Stronger

https://github.com/manishsoni1908/mobilenet-ssd-keras

SSD: Single Shot MultiBox Detector

https://github.com/manoranjan03/PaperImplementations

Deep Residual Learning for Image Recognition

https://github.com/manuelhaussmann/vgpmil

https://github.com/mapeAAU/3D-ZeF

3D-ZeF: A 3D Zebrafish Tracking Benchmark Dataset

https://github.com/mappy/tf-faster-rcnn

An Implementation of Faster RCNN with Study for Region Sampling

https://github.com/maragori/DeepfakeForensics-v1

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/marccoru/marinedebrisdetector

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

https://github.com/marcoancona/TorchPruner

Studying the Plasticity in Deep Convolutional Neural Networks using Random Pruning

https://github.com/marcointrovigne/WeatherDetection

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/marcusnerva/hmn

End-to-End Object Detection with Transformers

https://github.com/marinarierav-uab/foveabox

FoveaBox: Beyond Anchor-based Object Detector

https://github.com/mariosfourn/ScienceBowl

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/markson14/WheatDet

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

https://github.com/marlcplhra/ShuffleNet

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

https://github.com/marload/ConvNets-TensorFlow2

Going Deeper with Convolutions

https://github.com/marload/ImprovedResNet-TensorFlow2

Improved Residual Networks for Image and Video Recognition

https://github.com/martinsbruveris/tensorflow-image-models

Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions

https://github.com/martinzlocha/anchor-optimization

Improving RetinaNet for CT Lesion Detection with Dense Masks from Weak RECIST Labels

https://github.com/mary-phuong/multiexit-distillation

Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges

https://github.com/masamitsu-murase/deform_conv2d_onnx_exporter

Deformable ConvNets v2: More Deformable, Better Results

https://github.com/masoudnick/brain-tumor-mri-classification

Deep Residual Learning for Image Recognition

https://github.com/massucattoj/unet-model

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/masterhow/fishdreamer

Beyond the Field-of-View: Enhancing Scene Visibility and Perception with Clip-Recurrent Transformer

https://github.com/matakshay/Neural_Image_Caption_Generator

Deep Residual Learning for Image Recognition

https://github.com/matanbt/attack-tabular

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/matanrein/Understanding-Clouds-from-Satellite-Images

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/mateuszbuda/brain-segmentation-pytorch

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/mathieuorhan/darknet

YOLO9000: Better, Faster, Stronger

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