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/tensorflow/cleverhans

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/tensorflow/lingvo/blob/014007fbed690f6a10b51b630d7a4d0dd6cfd5df/lingvo/tasks/car/params/waymo_deepfusion.py

Learning an Uncertainty-Aware Object Detector for Autonomous Driving

https://github.com/tensorflow/models/tree/master/official/projects/detr

End-to-End Object Detection with Transformers

https://github.com/tensorflow/models/tree/master/official/vision

Mask R-CNN

https://github.com/tensorflow/models/tree/master/official/vision/beta/projects/yolo

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/tensorflow/models/tree/master/research/deeplab

Deep Residual Learning for Image Recognition

https://github.com/tensorflow/models/tree/master/research/object_detection

Speed/accuracy trade-offs for modern convolutional object detectors

https://github.com/tensorflow/models/tree/master/research/slim

Going Deeper with Convolutions

https://github.com/tensorflow/tpu/tree/master/models/official/detection

NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection

https://github.com/tensorflow/tpu/tree/master/models/official/detection/projects/self_training

Rethinking Pre-training and Self-training

https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/condconv

CondConv: Conditionally Parameterized Convolutions for Efficient Inference

https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet/mixnet

MixConv: Mixed Depthwise Convolutional Kernels

https://github.com/tensorpack/tensorpack

Deep Residual Learning for Image Recognition

https://github.com/tensorpack/tensorpack/tree/master/examples/FasterRCNN

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

https://github.com/tensorpack/tensorpack/tree/master/examples/ImageNetModels

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

https://github.com/tensorpack/tensorpack/tree/master/examples/ResNet

Deep Residual Learning for Image Recognition

https://github.com/ternaus/TernausNetV2

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/ternaus/angiodysplasia-segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/testestzxcv/DeblurGAN

DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks

https://github.com/thangvubk/SCNet

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/thanhtran98/yolov3_jetbot

YOLOv3: An Incremental Improvement

https://github.com/the-super-toys/glimpse-models

Deep Residual Learning for Image Recognition

https://github.com/theAIGuysCode/yolov3_deepsort

YOLOv3: An Incremental Improvement

https://github.com/thecryboy/DECONV

YOLOv3: An Incremental Improvement

https://github.com/thecryboy/yolov3_ultralytics_deconv

YOLOv3: An Incremental Improvement

https://github.com/theinmate4587/Autonomous-driving---Car-detection

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

https://github.com/thekauer/traindarknet

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/thekoshkina/blackai-challenge

YOLO9000: Better, Faster, Stronger

https://github.com/theostoican/Guided-Research/blob/main/finetuning_odin_resnet.ipynb

Object discovery and representation networks

https://github.com/thepooons/melanoma-comp-2020

ResNeSt: Split-Attention Networks

https://github.com/thesfinox/ml-cicy

Going Deeper with Convolutions

https://github.com/theswgong/MoNet

Geometric deep learning on graphs and manifolds using mixture model CNNs

https://github.com/theyellowdiary/openpose

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

https://github.com/theziqi/dccc

Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net

https://github.com/thinhhoang95/helipad-yolo

YOLOv3: An Incremental Improvement

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