https://github.com/ibaiGorordo/ONNX-YOLOv7-Object-Detection
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
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/idea-research/grounded-sam-2
https://github.com/iibrahimli/unet_sem_seg
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/imagesegmentation2020/indoor
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/imagine5am/ARU-Net
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/imistyrain/ssd
https://github.com/imistyrain/ssd-windows
https://github.com/inder9999/ISIC2017_skin_lesion_segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/inisis/caffe
https://github.com/irenazra/melanoma_segmentation_Unet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/irh-ca-team-car/attention-data
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning
https://github.com/it6aidl/outdoorsegmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/ivanwilliammd/I3DR-Net-Transfer-Learning
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/ivboh/3d_image_segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/jack16888/caffe-ssd
https://github.com/jack16888/caffessd
https://github.com/jakeret/tf_unet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/jakeret/unet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/jamiljami/Unet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/javiergarciamolina/selfie-background-removal
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/jbardelli/Petrographic-Image-Segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/jee-king/afnet
Frame-Event Alignment and Fusion Network for High Frame Rate Tracking
https://github.com/jellevankerk/Team-Challenge
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/jerichooconnell/tf_unet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/jh7cha/Towards-Real-Time-MOT
https://github.com/jhbrito/tracking_soccer_players
https://github.com/jiandai/mlTst
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/jiankaiwang/ioutracker
https://github.com/jimheaton/Ultra96_ML_Embedded_Workshop
https://github.com/jingcjie/FairMOT-mindspore-jupyter
FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking
https://github.com/jinlong17/advgps
V2V4Real: A Real-world Large-scale Dataset for Vehicle-to-Vehicle Cooperative Perception
https://github.com/jiportilla/edge-max-object-detector
https://github.com/jiye-ML/unet_pytorch
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/jizhu1023/DMAN_MOT
Online Multi-Object Tracking with Dual Matching Attention Networks
https://github.com/jjdiep/Multi-Object-Tracking
Probabilistic 3D Multi-Object Tracking for Autonomous Driving
https://github.com/jkhadley/capstone-project
U-Net: Convolutional Networks for Biomedical Image Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.