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/ubamba98/Brain-Segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/ucbdrive/3d-vehicle-tracking

Joint Monocular 3D Vehicle Detection and Tracking

https://github.com/uchile-robotics-forks/darknet_ros

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

https://github.com/uclaml/Padam

Deep Residual Learning for Image Recognition

https://github.com/ucsb-nlp-chang/textgrad

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/ucuapps/WSMIS

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/udacity/MLND-CN-Capstone-TGSImage

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/ugent-korea/pytorch-unet-segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/ujsyehao/mobilenetv3-ssd

Searching for MobileNetV3

https://github.com/ultralytics/ultralytics

DETRs Beat YOLOs on Real-time Object Detection

https://github.com/ultralytics/xview-yolov3

xView: Objects in Context in Overhead Imagery

https://github.com/ultralytics/yolov3

YOLOv3: An Incremental Improvement

https://github.com/ultralytics/yolov5

Deep Residual Learning for Image Recognition

https://github.com/umautobots/failing-to-learn

Failing to Learn: Autonomously Identifying Perception Failures for Self-driving Cars

https://github.com/umich-curly/3dmapping

MotionNet: Joint Perception and Motion Prediction for Autonomous Driving Based on Bird's Eye View Maps

https://github.com/umitkacar/ai-edge-computing

PP-PicoDet: A Better Real-Time Object Detector on Mobile Devices

https://github.com/unispac/visual-adversarial-examples-jailbreak-large-language-models

BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models

https://github.com/universea/SOLOv2

SOLOv2: Dynamic and Fast Instance Segmentation

https://github.com/unsky/FPN

Feature Pyramid Networks for Object Detection

https://github.com/unsky/RetinaNet

Focal Loss for Dense Object Detection

https://github.com/unsky/focal-loss

Focal Loss for Dense Object Detection

https://github.com/unveilx/slm-od-ml-comparison

Comparison Of Deep Object Detectors On A New Vulnerable Pedestrian Dataset

https://github.com/upc-postgrads/BrainScan

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/urbaneman/mmdetection_mobilenet

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/usnistgov/image-classification-resnet50

Deep Residual Learning for Image Recognition

https://github.com/usnistgov/image-regression-resnet50

Deep Residual Learning for Image Recognition

https://github.com/usnistgov/object-detection-yolov3

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

https://github.com/usnistgov/semantic-segmentation-fc-densenet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/usnistgov/semantic-segmentation-unet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/usnistgov/small-data-cnns

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/ustc-imcl/hst-for-compressed-image-sr

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/usuyama/pytorch-unet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/uvipen/ssd-pytorch

SSD: Single Shot MultiBox Detector

https://github.com/uw-hai/limeade

Deep Residual Learning for Image Recognition

https://github.com/v1viswan/Domain_adaptation_in_HRNet

Deep High-Resolution Representation Learning for Human Pose Estimation

https://github.com/vacancy/Synchronized-BatchNorm-PyTorch

MegDet: A Large Mini-Batch Object Detector

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