https://github.com/anoushkrit/Knowledge
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
- 10
- 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/anxingle/UNet-pytorch
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/aoru45/BasketNet
https://github.com/aoru45/LFFD-Pytorch
https://github.com/aparecidovieira/Road_extraction
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/aqbewtra/Multi-Class-Aerial-Segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/archit31uniyal/DC-Gnet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/argoverse/argoverse-api
https://github.com/aritzLizoain/Image-segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/arpytanshu/ssd-object-detection
https://github.com/arthurflor23/surface-crack-detection
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/arunm8489/Sort-Object-Tracking
https://github.com/arvind1998/SSD-Object-Detection
https://github.com/aschethor/Spline_PINN
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/aschethor/Unsupervised_Deep_Learning_of_Incompressible_Fluid_Dynamics
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/ashishpapanai/uNet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/ashwath007/amenity-detection
https://github.com/ashwath007/aminity-detection
https://github.com/asylve/Sea-Ice
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/atharva417/Yoga_Pose_GNR638
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/aubricot/object_detection_for_image_cropping
https://github.com/avanetten/avanetten.github.io
https://github.com/aws-samples/amazon-sagemaker-edge-defect-detection-computer-vision
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/ayoub-sudo/Attention-Unet-web-app
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/ayushdabra/drone-images-semantic-segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/ayushdabra/dubai-satellite-imagery-segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/ayushdabra/lunar-landscape-images-segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/badrbmb/cities-watch
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/bailvwangzi/repulsion_loss_ssd
https://github.com/baklawe/CoBe
CoBe -- Coded Beacons for Localization, Object Tracking, and SLAM Augmentation
https://github.com/balancap/SSD-Tensorflow
https://github.com/banus/umf_unet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/bayesian-object-tracking/dbot
https://github.com/bbednarski9/MaskTrackRCNN_DeepSort
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/bcmi/madisnet-inharmonious-region-localization
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/bellekci/Towards-Realtime-MOT-windows-py37
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.