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/rickyHong/py-faster-rcnn-repl

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

https://github.com/rickyHong/py-faster-rcnn-repl-cudnn5-support

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

https://github.com/rickyHong/pytorch-faster-rcnn-repl

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

https://github.com/rickyHong/tfRecord-Caltech256-repl2

Deep Residual Learning for Image Recognition

https://github.com/rickywrq/Progressive-Neural-Compression

Optimizing Edge Offloading Decisions for Object Detection

https://github.com/ridhaalkhabaz/YTVidObjectDectector

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

https://github.com/ridvansalihkuzu/hyperview_eagleeyes

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/rijuldhir/TSM

Non-local Neural Networks

https://github.com/rinnakk/japanese-clip

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/rishabmallick/Sign-language-live-predictor

SSD: Single Shot MultiBox Detector

https://github.com/rishigami/Swin-Transformer-TF

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/rishikc137/social-distancing-detector

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

https://github.com/rishikksh20/CoaT-pytorch

Co-Scale Conv-Attentional Image Transformers

https://github.com/rishikksh20/ResUnet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/rishizek/tensorflow-deeplab-v3

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/ritesh2448/Text-Detection-And-Recognition

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

https://github.com/rizalmaulanaa/robustness_of_prob_u_net

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/rjlallana/YOLO

Scaled-YOLOv4: Scaling Cross Stage Partial Network

https://github.com/rlagywns0213/cifar100_data_augmentation

Random Erasing Data Augmentation

https://github.com/rmcong/RRNet_TGRS2021

RRNet: Relational Reasoning Network with Parallel Multi-scale Attention for Salient Object Detection in Optical Remote Sensing Images

https://github.com/rmcong/fpnet_acmmm23

Frequency Perception Network for Camouflaged Object Detection

https://github.com/rmcong/picr-net_acmmm23

Point-aware Interaction and CNN-induced Refinement Network for RGB-D Salient Object Detection

https://github.com/rmwkwok/product_visual_search

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/rnirdhar/yoloMultiClass

YOLO9000: Better, Faster, Stronger

https://github.com/rnirdhar/yoloTestOneClass

YOLO9000: Better, Faster, Stronger

https://github.com/robbebluecp/tf2-yolov4

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/robertofranceschi/Image-classification-on-Caltech101-using-CNNs

Deep Residual Learning for Image Recognition

https://github.com/robin-ex/CMT

Hire-MLP: Vision MLP via Hierarchical Rearrangement

https://github.com/robin-ex/TokenFusion

Multimodal Token Fusion for Vision Transformers

https://github.com/robin-ex/ViG

Vision GNN: An Image is Worth Graph of Nodes

https://github.com/robin-ex/wave_mlp

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

https://github.com/robin-schneider/cicy-fourfolds

Going Deeper with Convolutions

https://github.com/robmarkcole/fire-detection-from-images

Wildfire Smoke Detection with Computer Vision

https://github.com/roboflow/rf-detr

Deformable DETR: Deformable Transformers for End-to-End Object Detection

https://github.com/rocapal/fish_detection

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

https://github.com/roeez/CalcificationDetection

Segmenting Microcalcifications in Mammograms and its Applications

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