https://github.com/JacobM184/EfficientNet-for-Gun-detection
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Papers with Code 아카이브
Fine-Grained Image Classification
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Fine-Grained Image Classification is a task in computer vision where the goal is to classify images into subcategories within a larger category. For example, classifying different species of birds or different types of flowers. This task is considered to be fine-grained because it requires the model to distinguish between subtle differences in visual appearance and patterns, making it more challenging than regular image classification tasks.
<span style="color:grey; opacity: 0.6">( Image credit: Looking for the Devil in the Details )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Jannoshh/simple-sam
Sharpness-Aware Minimization for Efficiently Improving Generalization
https://github.com/Janus-Shiau/SAM-tf2
Sharpness-Aware Minimization for Efficiently Improving Generalization
https://github.com/Jianlong-Fu/Multi-Attention-CNN
https://github.com/Jintao-Huang/EfficientNet_PyTorch
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/Jmak12/Iris1
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/JoegameZhou/efficientnet-b0
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/Julien-pour/music_classifcation
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/KakaoBrain/torchgpipe
GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism
https://github.com/KatherLab/HIA
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/Kevinz-code/CSRA
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/KiUngSong/Vision
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/Kurumi233/Mutual-Channel-Loss
The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification
https://github.com/LMaxence/Cifar10_Classification
https://github.com/Legoons/Melanoma_classification
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/LiAng199523/Multi-Attention-CNN-pytorch
https://github.com/LiYinqi/DIVE
https://github.com/MS-Mind/MS-Code-01/tree/main/mass
https://github.com/MS-Mind/MS-Code-02/tree/main/configs/bit
https://github.com/MS-Mind/MS-Code-06/tree/main/ntsnet
https://github.com/Mayurji/Image-Classification-PyTorch
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/Mechachleopteryx/SpinalNet
https://github.com/MicPie/clasp
Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision
https://github.com/Mind23-2/MindCode-1
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/Mind23-2/MindCode-101/tree/main/ntsnet
https://github.com/Mind23-2/MindCode-37
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/Mind23-2/MindCode-4/tree/main/resnet_ipl
https://github.com/Mind23-2/MindCode-60
https://github.com/Mind23-2/MindCode-89
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/MindCode-4/code-13/tree/main/Scalable-Sharpness-Aware-Minimization
Sharpness-Aware Minimization for Efficiently Improving Generalization
https://github.com/MindCode-4/code-13/tree/main/res_mlp_ms
ResMLP: Feedforward networks for image classification with data-efficient training
https://github.com/MindCode-4/code-8/tree/main/res_mlp_ms
ResMLP: Feedforward networks for image classification with data-efficient training
https://github.com/MindSpore-paper-code-2/code2/tree/main/ntsnet
https://github.com/MindSpore-paper-code-2/code400/tree/main/Efficientnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/MindSpore-paper-code-3/code1/tree/main/autoaugment
https://github.com/MindSpore-paper-code-3/code2/tree/main/darknet53
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.