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/cgebbe/kaggle_pku-autonomous-driving
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/charchit7/Using_Transoformers
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/chrisqqq123/FA-Dist-EfficientNet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/christiansafka/img2vec
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/ckyrkou/EmergencyNet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/clovaai/rexnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/code-implementation1/Code1/tree/main/DeiT
Training data-efficient image transformers & distillation through attention
https://github.com/code-implementation1/Code3/tree/main/efficientnet-b0
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/code-implementation1/Code6/tree/main/NFNet
https://github.com/code-implementation1/Code6/tree/main/ntsnet
https://github.com/code-implementation1/Code9/tree/main/regnet
https://github.com/code-implementation1/Code9/tree/main/resnest
https://github.com/cogtoolslab/physics-benchmarking-neurips2021
Training data-efficient image transformers & distillation through attention
https://github.com/conceptofmind/Parallel-ViT-flax
https://github.com/conceptofmind/ViT-haiku
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/cvjena/cs_parts
Classification-Specific Parts for Improving Fine-Grained Visual Categorization
https://github.com/danielpatrickhug/Research_Paper_Parser
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/darya-baranovskaya/keyword_spotting
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/davda54/sam
Sharpness-Aware Minimization for Efficiently Improving Generalization
https://github.com/davisking/dlib-models
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/denizyuret/playground
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/dispink/xpt
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/dreamercv/HSResNet-MMAL
Multi-branch and Multi-scale Attention Learning for Fine-Grained Visual Categorization
https://github.com/drumpt/ViT
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/duongttr/SWP
A Large-Scale Car Dataset for Fine-Grained Categorization and Verification
https://github.com/emla2805/vision-transformer
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/encounter1997/fp-detr
https://github.com/epoc88/PFLD_68pts_Pytorch_2020
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/eshoyuan/taop2021-3rd-solution
https://github.com/etetteh/sota-data-augmentation-and-optimizers
https://github.com/explainingai-code/VIT-Pytorch
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/fabio-sim/Depth-Anything-ONNX
https://github.com/facebookresearch/ClassyVision
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/facebookresearch/ClassyVision/tree/master/examples/vit
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/facebookresearch/hiera
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.