Papers with Code 아카이브
Human Part Segmentation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
- 6
- 보존된 평가 기록
- 19
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/miaohua1982/simple_fasterrcnn_pytorch
https://github.com/mic-dkfz/medicaldetectiontoolkit
https://github.com/mindspore-ai/models/tree/master/official/cv/maskrcnn
https://github.com/mirzaevinom/data_science_bowl_2018
https://github.com/mstfakts/building-detection-maskrcnn
https://github.com/multimodallearning/pytorch-mask-rcnn
https://github.com/muyistarsky/MaskRCNN
https://github.com/nikhithakarennagari/1311
https://github.com/nikhithakarennagari/Git
https://github.com/noelcodes/Mask_RCNN
https://github.com/o-evgeny/MRCNN_DeepFashion2
https://github.com/open-edge-platform/geti
https://github.com/open-edge-platform/training_extensions
https://github.com/paradisetechsoftsolutions/Deep-learning-model-using-Mask-R-CNN-for-predicting-AC-remote
https://github.com/phtruongan/py-faster-rcnn-docker
https://github.com/phykn/film-defect-detection
https://github.com/pj1920/mask-r-cnn
https://github.com/pmeletis/panoptic_parts
Cityscapes-Panoptic-Parts and PASCAL-Panoptic-Parts datasets for Scene Understanding
https://github.com/polospeter/TensorFlow-Advanced-Techniques-Specialization
https://github.com/pvdhove/owl-mask-rcnn
https://github.com/qilei123/pyfasterrcnn
https://github.com/qq330488563/TEST
https://github.com/quocdat32461997/Mask_RCNN
https://github.com/ravenwritingdesk/py-faster-rcnn-master
https://github.com/raymon-tian/hourglass-facekeypoints-detection
https://github.com/rh01/fast-rnn
https://github.com/rh01/faster-rcnn
https://github.com/rickyHong/py-faster-rcnn-repl
https://github.com/rickyHong/py-faster-rcnn-repl-cudnn5-support
https://github.com/saehan-choi/pixellib_auto_labelling
https://github.com/samsh19/ML_project
https://github.com/sbetageri/MaskRCNN
https://github.com/scolocke/Traffic_Sign_ID_GTSRB_GTSDB
https://github.com/soeaver/RP-R-CNN
https://github.com/soulguy/Faster-rcnn
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.