Papers with Code 아카이브
Object
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Replace the cat with a British Shorthair cat of the breed with bulging yellow eyes
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Pxtri2156/AdelaiDet_v2
https://github.com/PythonZhengbinChen/tf-faster-rcnn-cpu
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/Qengineering/MobileNetV1_SSD_OpenCV_Caffe
Speed/accuracy trade-offs for modern convolutional object detectors
https://github.com/Qengineering/MobileNetV2_YOLOV3_ncnn
https://github.com/Qengineering/MobileNet_SSD_OpenCV_TensorFlow
Speed/accuracy trade-offs for modern convolutional object detectors
https://github.com/Qengineering/NanoDet-ncnn-Jetson-Nano
https://github.com/Qengineering/NanoDet-ncnn-Raspberry-Pi-4
https://github.com/Qengineering/Rfcn_ncnn
R-FCN: Object Detection via Region-based Fully Convolutional Networks
https://github.com/Qengineering/TensorFlow_Lite_RPi_32-bits
Speed/accuracy trade-offs for modern convolutional object detectors
https://github.com/Qengineering/TensorFlow_Lite_RPi_64-bits
Speed/accuracy trade-offs for modern convolutional object detectors
https://github.com/Qengineering/TensorFlow_Lite_SSD_Jetson-Nano
Speed/accuracy trade-offs for modern convolutional object detectors
https://github.com/Qengineering/TensorFlow_Lite_SSD_RPi_32-bits
Speed/accuracy trade-offs for modern convolutional object detectors
https://github.com/Qengineering/TensorFlow_Lite_SSD_RPi_64-bits
Speed/accuracy trade-offs for modern convolutional object detectors
https://github.com/Qengineering/YoloV2-ncnn-Jetson-Nano
https://github.com/Qengineering/YoloV2-ncnn-Raspberry-Pi-4
https://github.com/Qengineering/YoloV3-ncnn-Raspberry-Pi-4
https://github.com/Qengineering/YoloV4-ncnn-Jetson-Nano
https://github.com/Qengineering/YoloV4-ncnn-Raspberry-Pi-4
https://github.com/Qengineering/YoloV4-ncnn-Raspberry-Pi-64-bit
https://github.com/Qinhj07/ATOMCode
https://github.com/RACHANAMOVVA09/CenterNet
https://github.com/RPFey/frustum-pointnets
https://github.com/Radhika009/CMPE_295B_MASTERPROJECT
3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction
https://github.com/Rahmanzia3/darknet
https://github.com/Rahmanzia3/darknet_original
https://github.com/Rahmanzia3/yolo
https://github.com/RaiyaniNirav/Mask-R-CNN-for-water-detection
https://github.com/Raj-08/tensorflow-object-contour-detection
Object Contour Detection with a Fully Convolutional Encoder-Decoder Network
https://github.com/RajArPatra/Super-OCR
https://github.com/Rakeshvd/Instance-Segmenation-of-MRI-scan-using-Mask-RCNN
https://github.com/Rakeshvd/Semantic-Segmenation-of-MRI-scan-using-Mask-RCNN
https://github.com/RangiLyu/mmdetection_test
RTMDet: An Empirical Study of Designing Real-Time Object Detectors
https://github.com/RangiLyu/nanodet
https://github.com/RashadGarayev/OpencvDnnmodule
https://github.com/RedWang1989/YOLOv4
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.