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/iejMac/ScriptWriter

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/ifrit98/bengaliai

Squeeze-and-Excitation Networks

https://github.com/iibrahimli/unet_sem_seg

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/ikh-innovation/roboweldar-votenet

Deep Hough Voting for 3D Object Detection in Point Clouds

https://github.com/ikigai-aa/Automatic-License-Plate-Recognition

A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector

https://github.com/ikigai-aa/Face-Mask-Detector-using-MobileNetV2

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/ikuokuo/start-scaled-yolov4

Scaled-YOLOv4: Scaling Cross Stage Partial Network

https://github.com/ikuto056/Estimating_Distance

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

https://github.com/ildoonet/pytorch-randaugment

RandAugment: Practical automated data augmentation with a reduced search space

https://github.com/imagesegmentation2020/indoor

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/imagine5am/ARU-Net

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/imistyrain/ssd

SSD: Single Shot MultiBox Detector

https://github.com/imistyrain/ssd-windows

SSD: Single Shot MultiBox Detector

https://github.com/imkoushik22/Walmart-Hackathon

Deep Residual Learning for Image Recognition

https://github.com/impiga/plain-detr

Swin Transformer V2: Scaling Up Capacity and Resolution

https://github.com/implus/CarDetectionExample

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/imrahulr/adversarial_robustness_pytorch

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/imshamsher/darknet

YOLOv3: An Incremental Improvement

https://github.com/inacmor/mobiledets-yolov4-pytorch

MobileDets: Searching for Object Detection Architectures for Mobile Accelerators

https://github.com/inder9999/ISIC2017_skin_lesion_segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/infini8-13/MaskRCNN_tryout

Mask R-CNN

https://github.com/infinitemugen/Attention-Conv-Pytorch

Attention Augmented Convolutional Networks

https://github.com/inisis/caffe

SSD: Single Shot MultiBox Detector

https://github.com/innat/External-Attention-TensorFlow

Beyond Self-attention: External Attention using Two Linear Layers for Visual Tasks

https://github.com/innat/HybridModel-GradCAM

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/innat/VideoMAE

Masked Autoencoders Are Scalable Vision Learners

https://github.com/innat/VideoSwin

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/insigh/Faster-RCNN-Tensorflow

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

https://github.com/insigh/Relation_Network_for_Objection

Relation Networks for Object Detection

https://github.com/intelligent-vehicles/bevdriver

Pyramid Scene Parsing Network

https://github.com/intelligentMachines-ITU/Blood-Cancer-Dataset-Lukemia-Attri-MICCAI-2024

A Large-scale Multi Domain Leukemia Dataset for the White Blood Cells Detection with Morphological Attributes for Explainability

https://github.com/interestingzhuo/foodretreival

Deep Residual Learning for Image Recognition

https://github.com/ioanvl/1d_squeeze_excitation

Squeeze-and-Excitation Networks

https://github.com/iphyer/DefectDetection-MaskRCNN

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

https://github.com/ipmi-icns-uke/sparsam

Emerging Properties in Self-Supervised Vision Transformers

https://github.com/irenazra/melanoma_segmentation_Unet

U-Net: Convolutional Networks for Biomedical Image Segmentation

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