Papers with Code 아카이브

Object Detection

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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/ruinmessi/ASFF

공식 코드

Learning Spatial Fusion for Single-Shot Object Detection

https://github.com/ruinmessi/RFBNet

공식 코드

Receptive Field Block Net for Accurate and Fast Object Detection

https://github.com/ruitian12/resformer

공식 코드

ResFormer: Scaling ViTs with Multi-Resolution Training

https://github.com/ruixing123/pdt_cwc_yolo-dp

공식 코드

PDT: Uav Target Detection Dataset for Pests and Diseases Tree

https://github.com/ruixxxx/sfuda

공식 코드

Unsupervised Cross-domain Pulmonary Nodule Detection without Source Data

https://github.com/ruiyang-061x/lise

공식 코드

Approaching Outside: Scaling Unsupervised 3D Object Detection from 2D Scene

https://github.com/ruiyangju/bone_fracture_detection_yolov8

공식 코드

Fracture Detection in Pediatric Wrist Trauma X-ray Images Using YOLOv8 Algorithm

https://github.com/ruiyangju/fce-yolov8

공식 코드

Pediatric Wrist Fracture Detection Using Feature Context Excitation Modules in X-ray Images

https://github.com/ruiyangju/fracture_detection_improved_yolov8

공식 코드

YOLOv8-AM: YOLOv8 Based on Effective Attention Mechanisms for Pediatric Wrist Fracture Detection

https://github.com/ruiyangju/yolov8_global_context_fracture_detection

공식 코드

Global Context Modeling in YOLOv8 for Pediatric Wrist Fracture Detection

https://github.com/ruiyangju/yolov9-fracture-detection

공식 코드

YOLOv9 for Fracture Detection in Pediatric Wrist Trauma X-ray Images

https://github.com/ruiyum/stone

공식 코드

STONE: A Submodular Optimization Framework for Active 3D Object Detection

https://github.com/ruizvitor/ANDA

공식 코드

ANDA: A Novel Data Augmentation Technique Applied to Salient Object Detection

https://github.com/rumsyx/RPCF

공식 코드

ROI Pooled Correlation Filters for Visual Tracking

https://github.com/runpeidong/pointdistiller

공식 코드

PointDistiller: Structured Knowledge Distillation Towards Efficient and Compact 3D Detection

https://github.com/ruoyuchen10/vps

공식 코드

Interpreting Object-level Foundation Models via Visual Precision Search

https://github.com/russellllaputa/MIRL

공식 코드

Masked Image Residual Learning for Scaling Deeper Vision Transformers

https://github.com/rvandeghen/ASTOD

공식 코드

Adaptive Self-Training for Object Detection

https://github.com/rwgai/rwds

공식 코드

Benchmarking Object Detectors under Real-World Distribution Shifts in Satellite Imagery

https://github.com/rwightman/pytorch-image-models

공식 코드

TResNet: High Performance GPU-Dedicated Architecture

https://github.com/ryoo72/una

공식 코드

Universal Noise Annotation: Unveiling the Impact of Noisy annotation on Object Detection

https://github.com/s4nyam/gradcam-yolo

공식 코드

Adversarial Attack On Yolov5 For Traffic And Road Sign Detection

https://github.com/sa4s-serc/adamls

공식 코드

Towards Self-Adaptive Machine Learning-Enabled Systems Through QoS-Aware Model Switching

https://github.com/sabrikhalil/micro_mobility_detection

공식 코드

Detection of Micromobility Vehicles in Urban Traffic Videos

https://github.com/sacmehta/EdgeNets

공식 코드

ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network

https://github.com/sadrultoaha/signboard-detection

공식 코드

Automatic Signboard Detection and Localization in Densely Populated Developing Cities

https://github.com/safednn-group/naptron

공식 코드

Detecting Out-of-distribution Objects Using Neuron Activation Patterns

https://github.com/safednn-group/safednn-clean

공식 코드

Combating noisy labels in object detection datasets

https://github.com/safetylab24/FusionCVCP

공식 코드

CVCP-Fusion: On Implicit Depth Estimation for 3D Bounding Box Prediction

https://github.com/safouaneelg/FusionVision

공식 코드

FusionVision: A comprehensive approach of 3D object reconstruction and segmentation from RGB-D cameras using YOLO and fast segment anything

https://github.com/safouaneelg/SRT2I

공식 코드

Class-Conditional self-reward mechanism for improved Text-to-Image models

https://github.com/sagerpascal/dynamic-link-architecture

공식 코드

The Cooperative Network Architecture: Learning Structured Networks as Representation of Sensory Patterns

https://github.com/sahalshajim/ss-owformer

공식 코드

Semi-supervised Open-World Object Detection

https://github.com/saic-fi/sslight

공식 코드

Effective Self-supervised Pre-training on Low-compute Networks without Distillation

https://github.com/saifkhichi96/ssbi-dataset

공식 코드

Enhanced Bank Check Security: Introducing a Novel Dataset and Transformer-Based Approach for Detection and Verification

https://github.com/sail-sg/poolformer

공식 코드

MetaFormer Is Actually What You Need for Vision

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