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/ai-med/squeeze_and_excitation

Squeeze-and-Excitation Networks

https://github.com/ai4ce/FLAT

Fooling LiDAR Perception via Adversarial Trajectory Perturbation

https://github.com/aibeedetect/bfjdet

CrowdHuman: A Benchmark for Detecting Human in a Crowd

https://github.com/aiha-lab/AI-thermometer

SSD: Single Shot MultiBox Detector

https://github.com/aiiu-lab/naturalistic-adversarial-patch

Re-labeling ImageNet: from Single to Multi-Labels, from Global to Localized Labels

https://github.com/aim-uofa/AdelaiDet

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/aim-uofa/adet

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/aimerykong/RGBT-detection

Multimodal Object Detection via Probabilistic Ensembling

https://github.com/aimerykong/predictive-filter-flow

Recurrent Pixel Embedding for Instance Grouping

https://github.com/aimotive/mm_training

aiMotive Dataset: A Multimodal Dataset for Robust Autonomous Driving with Long-Range Perception

https://github.com/aiot-mlsys-lab/famba-v

Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model

https://github.com/airalcorn2/paved2paradise

PointPillars: Fast Encoders for Object Detection from Point Clouds

https://github.com/airalcorn2/strike-with-a-pose

The Elephant in the Room

https://github.com/airctic/icevision

Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection

https://github.com/airplane2230/keras_Model_for_Beginner

SSD: Single Shot MultiBox Detector

https://github.com/airplane2230/keras_SSD_for_beginner

SSD: Single Shot MultiBox Detector

https://github.com/aisinai/seeg_analysis

Abnormal Event Detection in Videos using Spatiotemporal Autoencoder

https://github.com/ajayjain/vectorascent

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/ajaystar8/PDRUNet-PyTorch

Deep Residual Learning for Image Recognition

https://github.com/ajaystar8/UNet-PyTorch

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/ajcohen1/CoVision-Social-Distance-Violation-Detection

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/ajinkyakhoche/Object-Detection-Project

SSD: Single Shot MultiBox Detector

https://github.com/ajithvallabai/getsetgo_keras-beginner

Going Deeper with Convolutions

https://github.com/ajjdan/KaI

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/ajjdan/Karst-Segmentation-from-DEM

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/ajoshi944/Segmentation-severstal-steel

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/ajoshiusc/qrsegment

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/akamaster/pytorch_resnet_cifar10

Deep Residual Learning for Image Recognition

https://github.com/akar5h/Image-Recognition-with-CNN-and-Transfer-Learning-ResNet101

Deep Residual Learning for Image Recognition

https://github.com/akhadangi/EM-net

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/akinoriosamura/TorchSeg-mirror

BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation

https://github.com/akinsezer26/PersonDetection_TelegramBot

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/akkaze/cnn-without-any-downsampling

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/akommini/Spoken-Numeric-Digit-detection

Deep Residual Learning for Image Recognition

https://github.com/akrapukhin/MobileNetV3

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/akrsrivastava/Unet

U-Net: Convolutional Networks for Biomedical Image Segmentation

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