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/mirzaim/VisionGNN

Vision GNN: An Image is Worth Graph of Nodes

https://github.com/mit-han-lab/litepose

A ConvNet for the 2020s

https://github.com/mit-han-lab/mcunet

MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning

https://github.com/mit-han-lab/sparsevit

SparseViT: Revisiting Activation Sparsity for Efficient High-Resolution Vision Transformer

https://github.com/mit-han-lab/torchsparse

Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution

https://github.com/mit-spark/kiss-matcher

TEASER: Fast and Certifiable Point Cloud Registration

https://github.com/miteshksingh/SimplifiedYOLO

SSD: Single Shot MultiBox Detector

https://github.com/mitmedialab/unet4birds

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/mjseong0414/TANet

TANet: Robust 3D Object Detection from Point Clouds with Triple Attention

https://github.com/mkhalil1998/EC601_Group_Project

VinVL: Revisiting Visual Representations in Vision-Language Models

https://github.com/mkocabas/focal-loss-keras

Focal Loss for Dense Object Detection

https://github.com/mks0601/PoseFix_RELEASE

Deep High-Resolution Representation Learning for Human Pose Estimation

https://github.com/ml-inory/Caffe

SSD: Single Shot MultiBox Detector

https://github.com/ml-jku/cloob

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/mlbio-epfl/turtle

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/mlfoundations/open_clip

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/mlpc-ucsd/PRTR

Deep High-Resolution Representation Learning for Visual Recognition

https://github.com/mlvccn/bmtc_transferattackvid

Deep Residual Learning for Image Recognition

https://github.com/mmasana/FACIL

Class-incremental Learning via Deep Model Consolidation

https://github.com/mminamina/Transfer_Learning_NaimishNet---Keypoints_Detection

Deep Residual Learning for Image Recognition

https://github.com/mminamina/Transfer_Learning_ResNet18---Keypoints_Detection

Deep Residual Learning for Image Recognition

https://github.com/mnguyen0226/image-augmentation-dnn-performance

Deep Residual Learning for Image Recognition

https://github.com/mnicnc404/CartoonGan-tensorflow

ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design

https://github.com/mnikitin/ECANet

Squeeze-and-Excitation Networks

https://github.com/mnikitin/EfficientNet

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/mnikitin/channel-attention

Squeeze-and-Excitation Networks

https://github.com/moallafatma/Breast_Cancer_Detection_Classification

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

https://github.com/mobilesec/arcface-tensorflowlite

Deep Residual Learning for Image Recognition

https://github.com/moddy2024/resnet-9

Deep Residual Learning for Image Recognition

https://github.com/modelhub-ai/mobilenet

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/modelhub-ai/yolo-v3

YOLOv3: An Incremental Improvement

https://github.com/modelscope/modelscope

YOLOX-PAI: An Improved YOLOX, Stronger and Faster than YOLOv6

https://github.com/moein-shariatnia/OpenAI-CLIP

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/moein-shariatnia/Pix2Seq

Pix2seq: A Language Modeling Framework for Object Detection

https://github.com/moelgendy/mobileNetV2

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/moh833/VQA

Exploring Models and Data for Image Question Answering

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