https://github.com/MindSpore-paper-code-3/code9/tree/main/FasterRCNN
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Papers with Code 아카이브
Object Detection
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/MindSpore-paper-code-3/code9/tree/main/MaskRCNN
https://github.com/MindSpore-paper-code-3/code9/tree/main/ResNet
Rethinking Atrous Convolution for Semantic Image Segmentation
https://github.com/MindSpore-paper-code-3/code9/tree/main/SSD
https://github.com/MindSpore-paper-code-3/code9/tree/main/ShuffleNet
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
https://github.com/MindSpore-scientific-2/code-10/tree/main/STCrowd
STCrowd: A Multimodal Dataset for Pedestrian Perception in Crowded Scenes
https://github.com/MindSpore-scientific-2/code-10/tree/main/SV-X-Softmax
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/MindSpore-scientific-2/code-11/tree/main/STCrowd
STCrowd: A Multimodal Dataset for Pedestrian Perception in Crowded Scenes
https://github.com/MindSpore-scientific-2/code-12/tree/main/KPC-cF-main
KPCA-CAM: Visual Explainability of Deep Computer Vision Models using Kernel PCA
https://github.com/MindSpore-scientific-2/code-12/tree/main/group-normalization
https://github.com/MindSpore-scientific-2/code-14/tree/main/groupvit
GroupViT: Semantic Segmentation Emerges from Text Supervision
https://github.com/MindSpore-scientific-2/code-14/tree/main/mamba
Mamba YOLO: A Simple Baseline for Object Detection with State Space Model
https://github.com/MindSpore-scientific-2/code-2/tree/main/SING
https://github.com/MindSpore-scientific-2/code-3/tree/main/Sub2Full-OCT-Denoising
https://github.com/MindSpore-scientific-2/code-3/tree/main/WaveCRN-master
Wave-ViT: Unifying Wavelet and Transformers for Visual Representation Learning
https://github.com/MindSpore-scientific-2/code-4/tree/main/Deformable_Patch_Representation
https://github.com/MindSpore-scientific-2/code-4/tree/main/PolyLoss
PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions
https://github.com/MindSpore-scientific-2/code-4/tree/main/fisheradversarial-mindspore
Robust and Accurate Object Detection via Adversarial Learning
https://github.com/MindSpore-scientific-2/code-4/tree/main/rand_conv-master
RandAugment: Practical automated data augmentation with a reduced search space
https://github.com/MindSpore-scientific-2/code-5/tree/main/Deformable_Patch_Representation
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
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
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
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
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
https://github.com/MindSpore-scientific/code-14/tree/main/WaveCRN-master
ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.