https://github.com/1044197988/TF.Keras-Commonly-used-models
U-Net: Convolutional Networks for Biomedical Image Segmentation
Papers with Code 아카이브
2D Classification
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/111qqz/caffe-ssd
https://github.com/11aditya11/Neural_Style_Transfer
https://github.com/13274086/Tianchi2020
https://github.com/13520505/bigdataproj
News Session-Based Recommendations using Deep Neural Networks
https://github.com/13952522076/mmdet0
https://github.com/13952522076/mmdet1
https://github.com/1512159/tf-faster-rcnn-medico
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/15526837635/CenterPoint
https://github.com/15534081591/FairMOT
FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking
https://github.com/163GitHub/AI
https://github.com/16xccheng/keras-unet
UNet++: A Nested U-Net Architecture for Medical Image Segmentation
https://github.com/1716757342/mmsr
SymFormer: End-to-end symbolic regression using transformer-based architecture
https://github.com/1991yuyang/YOLOV1-PYTORCH
https://github.com/1hb6s7t/MindSpore-Uncertainty_Calibration_Object_Detection-
A Theoretical and Practical Framework for Evaluating Uncertainty Calibration in Object Detection
https://github.com/1hb6s7t/Super-Resolution-for-Root-Imaging-mindspore
https://github.com/2006pmach/awesome-few-shot-object-detection
Multi-Scale Positive Sample Refinement for Few-Shot Object Detection
https://github.com/2020132075/conmae
https://github.com/2023-MindSpore-1/ms-code-117
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/2023-MindSpore-1/ms-code-118
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/2023-MindSpore-1/ms-code-134
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/2023-MindSpore-1/ms-code-14
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
https://github.com/2023-MindSpore-1/ms-code-142
MSG-Transformer: Exchanging Local Spatial Information by Manipulating Messenger Tokens
https://github.com/2023-MindSpore-1/ms-code-143
Sparse Instance Activation for Real-Time Instance Segmentation
https://github.com/2023-MindSpore-1/ms-code-147
CIR-Net: Cross-modality Interaction and Refinement for RGB-D Salient Object Detection
https://github.com/2023-MindSpore-1/ms-code-149
EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision Applications
https://github.com/2023-MindSpore-1/ms-code-151
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/2023-MindSpore-1/ms-code-158
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/2023-MindSpore-1/ms-code-16/tree/main/ArbitraryStyleTransfer
https://github.com/2023-MindSpore-1/ms-code-16/tree/main/Contents
https://github.com/2023-MindSpore-1/ms-code-16/tree/main/augvit
How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers
https://github.com/2023-MindSpore-1/ms-code-161
https://github.com/2023-MindSpore-1/ms-code-162
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/2023-MindSpore-1/ms-code-167
Rethinking Atrous Convolution for Semantic Image Segmentation
https://github.com/2023-MindSpore-1/ms-code-168
Utilizing Self-supervised Representations for MOS Prediction
https://github.com/2023-MindSpore-1/ms-code-17/tree/main/CBAM
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.