Yolov7_StrongSORT_OSNet
PaddleDetection
| Yolov7_StrongSORT_OSNet | PaddleDetection | |
|---|---|---|
| 1 | 7 | |
| 393 | 12,074 | |
| - | 0.9% | |
| 0.0 | 6.5 | |
| 7 days ago | 8 days ago | |
| Python | Python | |
| GNU General Public License v3.0 only | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Yolov7_StrongSORT_OSNet
-
Multicamera ReID
I have several cameras that stream via multiprocessing frames. With yolov7 I detect persons. Now I want to track and re-identify these persons across all streams. From the multiprocessing point of view this is no problem (shared list) but I don't know which method I can/should use. I tried something with StrongSort but there is a Kalman filter in it which would iritate the model because of wrong positions and velocities. Can someone help me? At the moment this StrongSort algorithm from Github works great. But the ReID does not take place logically there across streams.
PaddleDetection
-
[R]DETRs Beat YOLOs on Real-time Object Detection
Our RTDETR-L achieves 53.0% AP on COCO val2017 and 114 FPS on T4 GPU, while RT-DETR-X achieves 54.8% AP and 74 FPS, outperforming all YOLO detectors of the same scale in both speed and accuracy. Furthermore, our RTDETR-R50 achieves 53.1% AP and 108 FPS, outperforming DINO-Deformable-DETR->R50 by 2.2% AP in accuracy and by about 21 times in FPS. Source code and pretrained models will be available at PaddleDetection1 (https://github.com/PaddlePaddle/PaddleDetection) .
-
YOLO series models ALL IN ONE
In order to make it easier for everyone to use the YOLO series model, we have open-sourced this collections. You can experience PP-YOLOE+, YOLOv8, RTMDet, DAMO-YOLO, YOLOv7, YOLOv6, YOLOX, YOLOv5...just in https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.5/docs/feature_models/PaddleYOLO_MODEL_en.md
- YOLO series Models ALL IN ONE
- Paddledetection - Object detection toolkit based on paddlepaddle
-
Baidu Researchers Propose PP-YOLOE Object Detector: an Evolved Version of YOLO Achieving SOTA Performance in Object Detection
Code for https://arxiv.org/abs/2203.16250 found: https://github.com/PaddlePaddle/PaddleDetection
Github: https://github.com/PaddlePaddle/PaddleDetection
- Imagine what historians will say about naming convention for pre trained models in 50 years…
What are some alternatives?
mmtracking - OpenMMLab Video Perception Toolbox. It supports Video Object Detection (VID), Multiple Object Tracking (MOT), Single Object Tracking (SOT), Video Instance Segmentation (VIS) with a unified framework.
mmdetection - OpenMMLab Detection Toolbox and Benchmark
yolo_tracking - BoxMOT: pluggable SOTA tracking modules for segmentation, object detection and pose estimation models
Object_Detection_Tracking - Out-of-the-box code and models for CMU's object detection and tracking system for multi-camera surveillance videos. Speed optimized Faster-RCNN model. Tensorflow based. Also supports EfficientDet. WACVW'20
faster-rcnn.pytorch - A faster pytorch implementation of faster r-cnn
ByteTrack - [ECCV 2022] ByteTrack: Multi-Object Tracking by Associating Every Detection Box
medicaldetectiontoolkit - The Medical Detection Toolkit contains 2D + 3D implementations of prevalent object detectors such as Mask R-CNN, Retina Net, Retina U-Net, as well as a training and inference framework focused on dealing with medical images.
SOLO - SOLO and SOLOv2 for instance segmentation, ECCV 2020 & NeurIPS 2020.
BCNet - Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers [CVPR 2021]
DeepSORT - support deepsort and bytetrack MOT(Multi-object tracking) using yolov5 with C++
multi-object-tracker - Multi-object trackers in Python