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/MindSpore-scientific-2/code-5/tree/main/Deformable_Patch_Representation

Deformable ConvNets v2: More Deformable, Better Results

https://github.com/MindSpore-scientific-2/code-5/tree/main/PolyLoss

PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions

https://github.com/MindSpore-scientific-2/code-5/tree/main/fisheradversarial-mindspore

Robust and Accurate Object Detection via Adversarial Learning

https://github.com/MindSpore-scientific-2/code-8/tree/main/PolyLoss

PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions

https://github.com/MindSpore-scientific-2/code-8/tree/main/SING

Single-Shot Refinement Neural Network for Object Detection

https://github.com/MindSpore-scientific-2/code-8/tree/main/STCrowd

STCrowd: A Multimodal Dataset for Pedestrian Perception in Crowded Scenes

https://github.com/MindSpore-scientific/code-1/tree/main/PolyLoss

PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions

https://github.com/MindSpore-scientific/code-10/tree/main/MicAugment

Dilated Neighborhood Attention Transformer

https://github.com/MindSpore-scientific/code-10/tree/main/PolyLoss

PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions

https://github.com/MindSpore-scientific/code-10/tree/main/SING

Single-Shot Refinement Neural Network for Object Detection

https://github.com/MindSpore-scientific/code-11/tree/main/PolyLoss

PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions

https://github.com/MindSpore-scientific/code-11/tree/main/SING

Single-Shot Refinement Neural Network for Object Detection

https://github.com/MindSpore-scientific/code-11/tree/main/Uncertainty_Calibration_Object_Detection

Uncertainty Calibration and its Application to Object Detection

https://github.com/MindSpore-scientific/code-12/tree/main/EPRNet

EPSANet: An Efficient Pyramid Squeeze Attention Block on Convolutional Neural Network

https://github.com/MindSpore-scientific/code-13/tree/main/PolyLoss

PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions

https://github.com/MindSpore-scientific/code-13/tree/main/SING

Single-Shot Refinement Neural Network for Object Detection

https://github.com/MindSpore-scientific/code-14/tree/main/WaveCRN-master

ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks

https://github.com/MindSpore-scientific/code-14/tree/main/res_mlp_ms

Res2Net: A New Multi-scale Backbone Architecture

https://github.com/MindSpore-scientific/code-14/tree/main/segmentation-dataset

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/MindSpore-scientific/code-14/tree/main/weakly-supervised

Weakly Supervised Deep Detection Networks

https://github.com/MindSpore-scientific/code-2/tree/main/squeeze-and-attention-networks-for-semantic

Squeeze-and-Excitation Networks

https://github.com/MindSpore-scientific/code-2/tree/main/ssFPN

Feature Pyramid Networks for Object Detection

https://github.com/MindSpore-scientific/code-3/tree/main/corss-dataset-training

Cross-dataset Training for Class Increasing Object Detection

https://github.com/MindSpore-scientific/code-5/tree/main/polyloss

PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions

https://github.com/MindSpore-scientific/code-7/tree/main/YOLOS

DETRs Beat YOLOs on Real-time Object Detection

https://github.com/MindSpore-scientific/code-7/tree/main/dino

Emerging Properties in Self-Supervised Vision Transformers

https://github.com/MindSpore-scientific/code-8/tree/main/MicAugment

Dilated Neighborhood Attention Transformer

https://github.com/MindSpore-scientific/code-8/tree/main/PolyLoss

PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions

https://github.com/MinghaoNJU/Object-detection-for-graphical-user-interface

Object Detection for Graphical User Interface: Old Fashioned or Deep Learning or a Combination?

https://github.com/Mingjian-Zhu/DyFPN

Dynamic Feature Pyramid Networks for Object Detection

https://github.com/MioChiu/MobileNet_V2_TensorFlow

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/MioChiu/ResNet_TensorFlow

Deep Residual Learning for Image Recognition

https://github.com/Miracle1991/DetectionHub

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

https://github.com/MishaLaskin/vq-vae

Deep Residual Learning for Image Recognition

https://github.com/MishaLaskin/vqvae

Deep Residual Learning for Image Recognition

https://github.com/MistyNight/AdaptiveDVO

Edge-Direct Visual Odometry

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