https://github.com/javiferran/document-classification
Improving accuracy and speeding up Document Image Classification through parallel systems
Papers with Code 아카이브
Document Image Classification
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Document image classification is the task of classifying documents based on images of their contents.
<span style="color:grey; opacity: 0.6">( Image credit: Real-Time Document Image Classification using Deep CNN and Extreme Learning Machines )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/kaushaltrivedi/fast-bert
https://github.com/kenAlparslan/Texttract
LayoutLM: Pre-training of Text and Layout for Document Image Understanding
https://github.com/knuddj1/op_text
https://github.com/knuddy/op_text
https://github.com/lashoun/hanna-benchmark-asg
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/beit
https://github.com/liuxingwt/CLS
Training data-efficient image transformers & distillation through attention
https://github.com/lulia0228/Document_IE
LayoutLM: Pre-training of Text and Layout for Document Image Understanding
https://github.com/lvyufeng/bert4ms
https://github.com/microsoft/unilm/tree/master/layoutlm
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/mthcom/hscore-dataset-pruning
https://github.com/musixmatchresearch/umberto
https://github.com/nachiket273/pytorch_resnet_rs
Revisiting ResNets: Improved Training and Scaling Strategies
https://github.com/nguyenvulebinh/vietnamese-roberta
https://github.com/nunenuh/layoutlm.pytorch
LayoutLM: Pre-training of Text and Layout for Document Image Understanding
https://github.com/nus-hpc-ai-lab/dyvm
Training data-efficient image transformers & distillation through attention
https://github.com/obi-ml-public/ehr_deidentification
https://github.com/octanove/shiba
https://github.com/omarsou/layoutlm_CORD
LayoutLM: Pre-training of Text and Layout for Document Image Understanding
https://github.com/omihub777/vit-cifar
Training data-efficient image transformers & distillation through attention
https://github.com/oneflow-inc/libai
https://github.com/open-edge-platform/geti
Training data-efficient image transformers & distillation through attention
https://github.com/open-edge-platform/training_extensions
Training data-efficient image transformers & distillation through attention
https://github.com/pengzhiliang/MAE-pytorch
https://github.com/pisalore/roberta_results
https://github.com/pwc-1/Paper-9/tree/main/5/xlm_roberta
https://github.com/pwc-1/Paper-9/tree/main/layoutlm
LayoutLM: Pre-training of Text and Layout for Document Image Understanding
https://github.com/pwc-1/Paper-9/tree/main/layoutlmv2
LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding
https://github.com/pwc-1/Paper-9/tree/main/layoutlmv3
LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking
https://github.com/pwc-1/Paper-9/tree/main/lilt
https://github.com/ricaelum42/Contextual-Twitter-Sarcasm-Detection
https://github.com/rwightman/pytorch-image-models
Training data-efficient image transformers & distillation through attention
https://github.com/s-chh/patchrot
Training data-efficient image transformers & distillation through attention
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.