https://github.com/mindspore-ai/models/tree/master/official/cv/vgg16
Very Deep Convolutional Networks for Large-Scale Image Recognition
Papers with Code 아카이브
General Classification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Algorithms trying to solve the general task of classification.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/mindspore-ai/models/tree/master/official/nlp/fasttext
https://github.com/mindspore-ai/models/tree/master/official/nlp/textcnn
https://github.com/mindspore-ai/models/tree/master/research/cv/AttentionCluster
Attention Clusters: Purely Attention Based Local Feature Integration for Video Classification
https://github.com/mindspore-ai/models/tree/master/research/cv/ProtoNet
https://github.com/mindspore-ai/models/tree/master/research/cv/ecolite
ECO: Efficient Convolutional Network for Online Video Understanding
https://github.com/mindspore-ai/models/tree/master/research/cv/inception_resnet_v2
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
https://github.com/mindspore-ai/models/tree/master/research/cv/ntsnet
https://github.com/mindspore-ai/models/tree/master/research/cv/pnasnet
https://github.com/mindspore-ai/models/tree/master/research/cv/resnet3d
Would Mega-scale Datasets Further Enhance Spatiotemporal 3D CNNs?
https://github.com/mindspore-ai/models/tree/master/research/cv/resnext152_64x4d
Aggregated Residual Transformations for Deep Neural Networks
https://github.com/mindspore-ai/models/tree/master/research/cv/single_path_nas
Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours
https://github.com/mindspore-ai/models/tree/master/research/cv/vgg19
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/mindspore-ai/models/tree/master/research/nlp/hypertext
https://github.com/mindspore-courses/ABSA-MindSpore
Effective LSTMs for Target-Dependent Sentiment Classification
https://github.com/mindspore-courses/DeepNLP-models-MindSpore
https://github.com/mindspore-courses/External-Attention-MindSpore/blob/main/model/attention/CBAM.py
https://github.com/mindspore-courses/External-Attention-MindSpore/blob/main/model/backbone/CrossViT.py
CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification
https://github.com/mindspore-courses/External-Attention-MindSpore/blob/main/model/backbone/LeViT.py
LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference
https://github.com/mindspore-courses/External-Attention-MindSpore/blob/main/model/backbone/resnext.py
Aggregated Residual Transformations for Deep Neural Networks
https://github.com/mindspore-courses/External-Attention-MindSpore/blob/main/model/conv/CondConv.py
CondConv: Conditionally Parameterized Convolutions for Efficient Inference
https://github.com/mindspore-courses/MindSpore-classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/googlenet.py
https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/pnasnet.py
https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/regnet.py
https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/shufflenetv1.py
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/vgg.py
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/mingyuw/tracker_209as
SiamFC++: Towards Robust and Accurate Visual Tracking with Target Estimation Guidelines
https://github.com/minhdua/PHONES
https://github.com/minhduchoang301/AmsText-OCR--website
https://github.com/minhduchoang301/OCR-community-website
https://github.com/minhto2802/keras-shufflenet
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
https://github.com/minhuanli/Multimodal-Mapping
Discovering a sparse set of pairwise discriminating features in high dimensional data
https://github.com/minoring/batch-norm-visualize
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
https://github.com/minoring/nin-tf2
https://github.com/minseop-aitrics/FewshotLearning
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.