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Efficient ViTs
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Increasing the efficiency of ViTs without the modification of the architecture. (i.e., Key & Query Sparsification, Token pruning & merging)
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/NVlabs/A-ViT
https://github.com/NVlabs/NViT
Global Vision Transformer Pruning with Hessian-Aware Saliency
https://github.com/Oguzhanercan/Vision-Transformers
Training data-efficient image transformers & distillation through attention
https://github.com/PaddlePaddle/PASSL
All Tokens Matter: Token Labeling for Training Better Vision Transformers
https://github.com/PaddlePaddle/PLSC/tree/master/task/accelerate/tome
https://github.com/PaddlePaddle/PLSC/tree/master/task/classification/deit
Training data-efficient image transformers & distillation through attention
https://github.com/PaddlePaddle/PaddleClas
Training data-efficient image transformers & distillation through attention
https://github.com/TACJu/TransFG
Training data-efficient image transformers & distillation through attention
https://github.com/UdbhavPrasad072300/Transformer-Implementations
Training data-efficient image transformers & distillation through attention
https://github.com/Westlake-AI/openmixup
https://github.com/affjljoo3581/deit3-jax
Training data-efficient image transformers & distillation through attention
https://github.com/ahmedelmahy/myownvit
Training data-efficient image transformers & distillation through attention
https://github.com/aiot-mlsys-lab/famba-v
Training data-efficient image transformers & distillation through attention
https://github.com/alessiomora/unlearning_fl
Training data-efficient image transformers & distillation through attention
https://github.com/alibaba/EasyCV
Training data-efficient image transformers & distillation through attention
https://github.com/asrafulashiq/deit-custom
Training data-efficient image transformers & distillation through attention
https://github.com/bshantam97/Attention_Based_Networks
Training data-efficient image transformers & distillation through attention
https://github.com/catalpaaa/demansia
All Tokens Matter: Token Labeling for Training Better Vision Transformers
https://github.com/code-implementation1/Code1/tree/main/DeiT
Training data-efficient image transformers & distillation through attention
https://github.com/cogtoolslab/physics-benchmarking-neurips2021
Training data-efficient image transformers & distillation through attention
https://github.com/dbolya/tomesd
https://github.com/flytocc/TokenLabeling-paddle
All Tokens Matter: Token Labeling for Training Better Vision Transformers
https://github.com/gatech-eic/vitcod
Training data-efficient image transformers & distillation through attention
https://github.com/holdfire/CLS
Training data-efficient image transformers & distillation through attention
https://github.com/holdfire/FAS
Training data-efficient image transformers & distillation through attention
https://github.com/huggingface/transformers
Training data-efficient image transformers & distillation through attention
https://github.com/hustvl/vim
Training data-efficient image transformers & distillation through attention
https://github.com/jacobgil/vit-explain
Training data-efficient image transformers & distillation through attention
https://github.com/liuxingwt/CLS
Training data-efficient image transformers & distillation through attention
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/moein-shariatnia/Pix2Seq
Training data-efficient image transformers & distillation through attention
https://github.com/naver-ai/vidt
All Tokens Matter: Token Labeling for Training Better Vision Transformers
https://github.com/nus-hpc-ai-lab/dyvm
Training data-efficient image transformers & distillation through attention
https://github.com/omihub777/vit-cifar
Training data-efficient image transformers & distillation through attention
https://github.com/open-edge-platform/geti
Training data-efficient image transformers & distillation through attention
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