Papers with Code 아카이브

2D Object Detection

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

15

평가표

20

보존된 평가 기록

57

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/hosshonarvar/Image-Segmentation

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

https://github.com/hott-j/face-mask-detection

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/hou-yz/multiviewx

Multiview Detection with Feature Perspective Transformation

https://github.com/houqb/DSS

Deeply supervised salient object detection with short connections

https://github.com/houseleo/pointnet

Frustum PointNets for 3D Object Detection from RGB-D Data

https://github.com/houssemjebari/Fruit-Detection

Mask R-CNN

https://github.com/houstonsantos/CassavaLeafDisease

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/hova88/Lidardet

PointPillars: Fast Encoders for Object Detection from Point Clouds

https://github.com/hova88/PointPillars_MultiHead_40FPS

PointPillars: Fast Encoders for Object Detection from Point Clouds

https://github.com/howBiGaStorm/ZeroShot-YOLO

Zero-Shot Detection

https://github.com/howardyclo/maskrcnn-benchmark

RetinaMask: Learning to predict masks improves state-of-the-art single-shot detection for free

https://github.com/hpc203/YOLObile-opencv-dnn

YOLObile: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-Design

https://github.com/hrdwsong/TDLMR2AA-Paddle

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/hsd1503/resnet1d

Deep Residual Learning for Image Recognition

https://github.com/hsfzxjy/swavx

End-to-End Object Detection with Transformers

https://github.com/hsinyilin19/resnetvae

Deep Residual Learning for Image Recognition

https://github.com/hthierno/pytorch-stardist

Star-convex Polyhedra for 3D Object Detection and Segmentation in Microscopy

https://github.com/huaifeng1993/JinNanCompetition2

RetinaMask: Learning to predict masks improves state-of-the-art single-shot detection for free

https://github.com/hualuluu/--every-day-paper--

Object Detection based on Region Decomposition and Assembly

https://github.com/huan123/py-fatser-rcnn

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

https://github.com/huangleiBuaa/IterNorm-pytorch

Iterative Normalization: Beyond Standardization towards Efficient Whitening

https://github.com/huangyebiaoke/R-CNN

Deep Learning Based Steel Pipe Weld Defect Detection

https://github.com/huanranchen/CommonWeaknessDetection

Rethinking Model Ensemble in Transfer-based Adversarial Attacks

https://github.com/huchuanlu/15_7

Inner and Inter Label Propagation: Salient Object Detection in the Wild

https://github.com/huggingface/alignment-handbook

Zephyr: Direct Distillation of LM Alignment

https://github.com/huggingface/datasets/tree/master/datasets/cppe-5

CPPE-5: Medical Personal Protective Equipment Dataset

https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/maxxvit.py

MaxViT: Multi-Axis Vision Transformer

https://github.com/huggingface/transformers

End-to-End Object Detection with Transformers

https://github.com/hukaixuan19970627/yolov5_obb

On the Arbitrary-Oriented Object Detection: Classification based Approaches Revisited

https://github.com/hukefei/SOLO-master

SOLOv2: Dynamic and Fast Instance Segmentation

https://github.com/hukefei/taobaolive

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/hula-ai/DAMA

Exploring Plain Vision Transformer Backbones for Object Detection

https://github.com/hunglc007/tensorflow-yolov4-tflite

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/hunter10bt/DeepLearningFinalPresentation

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/hunto/image_classification_sota

Diverse Branch Block: Building a Convolution as an Inception-like Unit

https://github.com/hustic/TinyImageSet

Deep Residual Learning for Image Recognition

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