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/jinbac/PlantNet

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/jinbac/Yolo_Trees

TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-captured Scenarios

https://github.com/jinfagang/manadet

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/jinfagang/nb

Micro-Batch Training with Batch-Channel Normalization and Weight Standardization

https://github.com/jingcjie/FairMOT-mindspore-jupyter

FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking

https://github.com/jinlong17/CoMamba

CoMamba: Real-time Cooperative Perception Unlocked with State Space Models

https://github.com/jinsheng124/yolox

YOLOX: Exceeding YOLO Series in 2021

https://github.com/jiportilla/edge-max-object-detector

SSD: Single Shot MultiBox Detector

https://github.com/jireh-father/mmdetection_210507

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/jiwanchung/vlis

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

https://github.com/jiwei0921/CoNet

Accurate RGB-D Salient Object Detection via Collaborative Learning

https://github.com/jiwei0921/DCF

https://github.com/jiwei0921/DMRA

https://github.com/jiwei0921/Saliency-Evaluation-Toolbox

Disentangled High Quality Salient Object Detection

https://github.com/jiweibo/imagenet

Deep Residual Learning for Image Recognition

https://github.com/jiye-ML/unet_pytorch

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/jkboyce/hawkeye

YOLO9000: Better, Faster, Stronger

https://github.com/jkhadley/capstone-project

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/jklife3/maskrcnn-impl

Mask R-CNN

https://github.com/jkvt2/hierloss

YOLO9000: Better, Faster, Stronger

https://github.com/jmiemirza/actmad

AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty

https://github.com/jmjeon94/MobileNet-Pytorch

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/jmnolte/hccnet

Emerging Properties in Self-Supervised Vision Transformers

https://github.com/jmpap/yolov2-tensorflow-2.0

YOLO9000: Better, Faster, Stronger

https://github.com/jms0923/mmdet_tta

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/jmysu/ESP32CAM-FaceDetect-Line-Notify

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/jndeng/DACSDC-DeepZ

Feature Pyramid Networks for Object Detection

https://github.com/jockeydj/mindspore_models/tree/main/shufflenetv2

ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design

https://github.com/jodumagpi/Xray-ObjSep-v1

Mask R-CNN

https://github.com/joeynavarro/facial_expression_classification

Deep Residual Learning for Image Recognition

https://github.com/johangenis/TF2.0-Advanced-Transfer-Learning-Project-1

ImageNet Large Scale Visual Recognition Challenge

https://github.com/joheras/darknet-colab

YOLO9000: Better, Faster, Stronger

https://github.com/john-mnz/ComfyUI-Inspyrenet-Rembg

Revisiting Image Pyramid Structure for High Resolution Salient Object Detection

https://github.com/johngear/eecs504

Deep Residual Learning for Image Recognition

https://github.com/johnnylu305/resnet-50-101-152

Deep Residual Learning for Image Recognition

https://github.com/johnsebin97/darknet

YOLOv3: An Incremental Improvement

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