https://github.com/matanrein/Understanding-Clouds-from-Satellite-Images
U-Net: Convolutional Networks for Biomedical Image Segmentation
Papers with Code 아카이브
Object Tracking
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
- 10
- 보존된 평가 기록
- 43
Object tracking is the task of taking an initial set of object detections, creating a unique ID for each of the initial detections, and then tracking each of the objects as they move around frames in a video, maintaining the ID assignment. State-of-the-art methods involve fusing data from RGB and event-based cameras to produce more reliable object tracking. CNN-based models using only RGB images as input are also effective. The most popular benchmark is OTB. There are several evaluation metrics specific to object tracking, including HOTA, MOTA, IDF1, and Track-mAP.
<span style="color:grey; opacity: 0.6">( Image credit: Towards-Realtime-MOT )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/mateuszbuda/brain-segmentation-pytorch
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/matteo-dunnhofer/SkiTB-toolkit
https://github.com/maxpark/SkyNet-1
SkyNet: a Hardware-Efficient Method for Object Detection and Tracking on Embedded Systems
https://github.com/mberkay0/pretrained-backbones-unet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/mdv3101/DeepSort_Yolo
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/medchaabane/static_objects_geolocalization
End-to-end Learning Improves Static Object Geo-localization in Monocular Video
https://github.com/megvii-research/caddm
https://github.com/megvii-research/petr
Cross Modal Transformer: Towards Fast and Robust 3D Object Detection
https://github.com/melissande/dhi-segmentation-buildings
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/mfp0610/semantic-segmentaion
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/microsoft/FairMOT
FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking
https://github.com/microsoft/UDA
FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking
https://github.com/mikel-brostrom/Yolov3_DeepSort_Pytorch
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/mikel-brostrom/Yolov5_DeepSort_Pytorch
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/mikel-brostrom/boxmot
ByteTrack: Multi-Object Tracking by Associating Every Detection Box
https://github.com/mikel-brostrom/yolov7
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
https://github.com/mikel-brostrom/yolov7_strongsort_osnet
https://github.com/milesial/Pytorch-UNet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/millanp95/OFTracker
https://github.com/mindspore-ai/models/blob/master/research/cv/siamRPN
https://github.com/mindspore-ai/models/tree/master/official/cv/Deepsort
https://github.com/mindspore-ai/models/tree/master/official/cv/ssd
https://github.com/mindspore-ai/models/tree/master/official/cv/unet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/mindspore-ai/models/tree/master/research/cv/STGAN
https://github.com/mindspore-ai/models/tree/master/research/cv/fairmot
FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking
https://github.com/mindspore-ai/models/tree/master/research/cv/ssd_resnet50
https://github.com/mindspore-ai/models/tree/master/research/cv/tsm
TSM: Temporal Shift Module for Efficient Video Understanding
https://github.com/minerva-ml/open-solution-mapping-challenge
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/mingyuw/tracker_209as
SiamFC++: Towards Robust and Accurate Visual Tracking with Target Estimation Guidelines
https://github.com/minhhuu291/Logo-Recognition
https://github.com/mirnadaye/Brain_Tumor_Project
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/misterio818/Single-Object-Tracking
Single Object Tracking: A Survey of Methods, Datasets, and Evaluation Metrics
https://github.com/miteshksingh/SimplifiedYOLO
https://github.com/mitmedialab/unet4birds
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/mkang315/rcs-yolo
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.