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/kingcong/convnext-

A ConvNet for the 2020s

https://github.com/kingcong/googlenet

Going Deeper with Convolutions

https://github.com/kingcong/gpu_FaceAttribute

Deep Residual Learning for Image Recognition

https://github.com/kingcong/gpu_HRNetW48_cls

Deep High-Resolution Representation Learning for Visual Recognition

https://github.com/kingcong/gpu_ISyNet

ISyNet: Convolutional Neural Networks design for AI accelerator

https://github.com/kingcong/gpu_SE-Net

Deep Residual Learning for Image Recognition

https://github.com/kingcong/mobilenetv2

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/kingcong/models/tree/main/wave_mlp

An Image Patch is a Wave: Phase-Aware Vision MLP

https://github.com/kingcong/res2net

Res2Net: A New Multi-scale Backbone Architecture

https://github.com/kingcong/resnet

Deep Residual Learning for Image Recognition

https://github.com/kingcong/u2net

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

https://github.com/kingcong/vit

How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers

https://github.com/kira-95/adl_cancer_detection

Detecting Cancer Metastases on Gigapixel Pathology Images

https://github.com/kiranrgupta26/ObjectDetection

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

https://github.com/kiranu1024/deep-learning

Deep Residual Learning for Image Recognition

https://github.com/kirilcvetkov92/Semantic-Segmentation

BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation

https://github.com/kirilcvetkov92/Vehicle-Detection

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

https://github.com/kiruthick101/darknet

YOLO9000: Better, Faster, Stronger

https://github.com/kishanAk21/Skin-Cancer-Image-Classification

Deep Residual Learning for Image Recognition

https://github.com/kishorkuttan/traffic_net

Deep Residual Learning for Image Recognition

https://github.com/kisna-aryan/YOLOX

YOLOX: Exceeding YOLO Series in 2021

https://github.com/kit-mrt/masked-fusion-360

Masked Autoencoders Are Scalable Vision Learners

https://github.com/kittyvarghese/lightweight_saliency_detection

SaLite : A light-weight model for salient object detection

https://github.com/kjybinp/SCGAN

Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal

https://github.com/kkahatapitiya/X3D-Multigrid

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/kkhoot/PAA_Faster-RCNN

Probabilistic Anchor Assignment with IoU Prediction for Object Detection

https://github.com/klemens-floege/oneprot

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/klin059/lits

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/km1414/CNN-models

Going Deeper with Convolutions

https://github.com/kmaninis/COB

Convolutional Oriented Boundaries: From Image Segmentation to High-Level Tasks

https://github.com/kmt112/probable-lamp

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/kmzzhang/nbi

Deep Residual Learning for Image Recognition

https://github.com/kobiso/CBAM-keras

Squeeze-and-Excitation Networks

https://github.com/kobiso/CBAM-tensorflow

Squeeze-and-Excitation Networks

https://github.com/kobiso/CBAM-tensorflow-slim

Squeeze-and-Excitation Networks

https://github.com/kobiso/FilterResponseNorm-MXNet

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

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