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/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/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

https://github.com/hustvl/YOLOP

YOLOP: You Only Look Once for Panoptic Driving Perception

https://github.com/husty530/Yamashita-darknet

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/huyhieupham/learning-from-multiple-annotators

Weighted boxes fusion: Ensembling boxes from different object detection models

https://github.com/huynhtuan17ti/AI4VN-Hackathon2020

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/huytranvan2010/RetinaNet-Understanding

Focal Loss for Dense Object Detection

https://github.com/huytranvan2010/SSD

SSD: Single Shot MultiBox Detector

https://github.com/huyz1117/Non_Local_Net_TensorFlow

Non-local Neural Networks

https://github.com/huzpsb/EffTransfer

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/hweersot/darknet-custom

YOLO9000: Better, Faster, Stronger

https://github.com/hwillard98/htmlify-yolo-training

YOLOv3: An Incremental Improvement

https://github.com/hx19940102/Image-Segmentation-based-on-FCN

Deep Residual Learning for Image Recognition

https://github.com/hy-signal/VoxelNet

VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection

https://github.com/hyang0129/foodclassapp

Focal Loss for Dense Object Detection

https://github.com/hycis/TensorGraph

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/hydrogo/rainnet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/hyh21521038/ssd-plate_detection

SSD: Single Shot MultiBox Detector

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