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/davidhalladay/MMDetection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/davidsvy/hard-negative-mixing

Hard Negative Mixing for Contrastive Learning

https://github.com/dawnlh/awesome-low-light-image-enhancement

Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement

https://github.com/daxiaHuang/brainstorm

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/daxiapazi/faster-rcnn

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

https://github.com/dayfixer/darknet-comment

YOLOv3: An Incremental Improvement

https://github.com/dbailey00/Semantic-Segmentation-With-Seismic-Images

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/dbtmpl/Solving-Visual-Object-Ambiguities-when-Pointing

Solving Visual Object Ambiguities when Pointing: An Unsupervised Learning Approach

https://github.com/dc17540/darknet

YOLO9000: Better, Faster, Stronger

https://github.com/dc3ea9f/RegionViT

RegionViT: Regional-to-Local Attention for Vision Transformers

https://github.com/dcdcvgroup/LADS-mindspore

Referring Expression Comprehension Using Language Adaptive Inference

https://github.com/dead-mazai/avia-missing-airplanes

Deep Residual Learning for Image Recognition

https://github.com/debidatta/syndata-generation

Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection

https://github.com/dec880126/Self-driving-with-YOLO

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/dec880126/YOLOv4-Self-driving

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/decentNick/Signals_big_lab_SPBSTU

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/decile-team/cords

Learning From Less Data: A Unified Data Subset Selection and Active Learning Framework for Computer Vision

https://github.com/deep-learning-algorithm/LightWeightCNN

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/deeplearnproject/darknet

YOLO9000: Better, Faster, Stronger

https://github.com/deepmind/deepmind-research/tree/master/transporter

Unsupervised Learning of Object Keypoints for Perception and Control

https://github.com/deepmind/dm-haiku

Deep Residual Learning for Image Recognition

https://github.com/deepmind/kinetics-i3d

Going Deeper with Convolutions

https://github.com/deepmodeling/deepmd-kit

Deep Residual Learning for Image Recognition

https://github.com/deepquestai/fire-smoke-dataset

Deep Residual Learning for Image Recognition

https://github.com/delixing/Data-Augmentation

Augmentation for small object detection

https://github.com/delldu/MaskRCNN

Mask R-CNN

https://github.com/delmalih/MIAS-mammography-obj-detection

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

https://github.com/delta6189/Anime-Sketch-Colorizer

Micro-Batch Training with Batch-Channel Normalization and Weight Standardization

https://github.com/demul/ResidualNet

Deep Residual Learning for Image Recognition

https://github.com/dengpingfan/fsgan

U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection

https://github.com/denizyuret/playground

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/deolipankaj/Stone_Detection_MRCNN

Mask R-CNN

https://github.com/deontaepharr/Residual-Attention-Network

Deep Residual Learning for Image Recognition

https://github.com/depexo/BigDL-master

BigDL: A Distributed Deep Learning Framework for Big Data

https://github.com/dereyly/mmdet_sota

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/destinyls/SMOKE

SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation

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