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/maragori/DeepfakeForensics-v1
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/marcointrovigne/WeatherDetection
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/marrlab/dinobloom
https://github.com/martinsbruveris/tensorflow-image-models
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/mashaan14/VisionTransformer-MNIST
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/maxwelltsai/DeepGalaxy
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/mdmhriday/vision-transformers
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/megvii-research/basecls/tree/main/zoo/public/effnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/megvii-research/basecls/tree/main/zoo/public/resmlp
ResMLP: Feedforward networks for image classification with data-efficient training
https://github.com/megvii-research/basecls/tree/main/zoo/public/vit
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/meowbutlerdev/ViT
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/mhassann22/GCSAM
Gradient Centralization: A New Optimization Technique for Deep Neural Networks
https://github.com/microsoft/cream
https://github.com/mindspore-ai/models/blob/master/research/cv/efficientnet-b0
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/mindspore-ai/models/blob/master/research/cv/vit_base/
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/mindspore-ai/models/tree/master/official/cv/vit
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/mindspore-ai/models/tree/master/research/cv/TNT
https://github.com/mindspore-ai/models/tree/master/research/cv/ntsnet
https://github.com/mindspore-courses/External-Attention-MindSpore/blob/main/model/backbone/DeiT.py
Training data-efficient image transformers & distillation through attention
https://github.com/mindspore-courses/External-Attention-MindSpore/blob/main/model/backbone/TnT.py
https://github.com/mindspore-courses/External-Attention-MindSpore/blob/main/model/conv/MBConv.py
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/efficientnet.py
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/vit.py
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/minfengUCAS/Muti-Attention-CNN
https://github.com/mingsun-tse/good-da-in-kd
https://github.com/mingxingtan/efficientnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/miramind/efficientnets_pytorch
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/mnikitin/EfficientNet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/mnikitin/Gradient-Centralization
Gradient Centralization: A New Optimization Technique for Deep Neural Networks
https://github.com/mobulan/MPSA
https://github.com/mobulan/ielt
https://github.com/modeeric/eegvit-tcnet
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/moein-shariatnia/Pix2Seq
Training data-efficient image transformers & distillation through attention
https://github.com/mohammedsb/dinov2formedical
https://github.com/monsterzhzh/hrn
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.