https://github.com/PythonZhengbinChen/tf-faster-rcnn-cpu
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Papers with Code 아카이브
2D Object Detection
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 15
- 평가표
- 20
- 보존된 평가 기록
- 57
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Qdriving/Swin-TransformerV2-in-PP
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/PeleeNet_SSD
Pelee: A Real-Time Object Detection System on Mobile Devices
https://github.com/Qengineering/Rfcn_ncnn
R-FCN: Object Detection via Region-based Fully Convolutional Networks
https://github.com/Qengineering/ShuffleNetV2-ncnn
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
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-Jetson-Nano
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/QiaoranC/tf_ResNeSt_RegNet_model
CBNet: A Novel Composite Backbone Network Architecture for Object Detection
https://github.com/Qinhj07/ATOMCode
https://github.com/RACHANAMOVVA09/CenterNet
https://github.com/RDXiaoLu/MambaCoral-DiffDet
https://github.com/RDXiaoLu/SCoralDet-Dataset
https://github.com/RPFey/frustum-pointnets
https://github.com/RPraneetha/MoCo-FAIR
Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination
https://github.com/Radar-Camera-Fusion/Awesome-Radar-Perception
https://github.com/Raghvender1205/ConvNeXt
https://github.com/Rahmanzia3/darknet
https://github.com/Rahmanzia3/darknet_original
https://github.com/Rahmanzia3/yolo
https://github.com/RainbowNebula/PETS
Periodically Exchange Teacher-Student for Source-Free Object Detection
https://github.com/RaiyaniNirav/Mask-R-CNN-for-water-detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.