mmtracking
mmdeploy
mmtracking | mmdeploy | |
---|---|---|
7 | 4 | |
3,382 | 2,524 | |
1.6% | 2.5% | |
1.5 | 7.9 | |
8 months ago | about 1 month ago | |
Python | Python | |
Apache License 2.0 | 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.
mmtracking
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Tracking sets of Keypoints by Person
I suggest to try the top down approach with the https://openmmlab.com/ open source package. The openmmlab provides multiple algorithms, datasets and pretrained models for various computer vision tasks. Start with mmpose video demo that integrates detection and pose estimation. You can add later tracking with https://github.com/open-mmlab/mmtracking to track the poses in time.
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MMDeploy: Deploy All the Algorithms of OpenMMLab
MMTracking: OpenMMLab video perception toolbox and benchmark.
- [P]We have supported Quasi-Dense Similarity Learning for Multiple Object Tracking.
- [p]We have supported Quasi-Dense Similarity Learning for Multiple Object Tracking
- MMTracking have supported Quasi-Dense Similarity Learning for Multiple Object Tracking.
- MMTracking Supports Quasi-Dense Similarity Learning for Multiple Object Tracking
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Help combining custom detector (yolo) with a tracker.
Implementations exist, like https://github.com/open-mmlab/mmtracking
mmdeploy
- [D] Object detection models that can be easily converted to CoreML
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Orange Pi 5 Plus Koboldcpp Demo (MPT, Falcon, Mini-Orca, Openllama)
The RK3588 also has a NPU for accelerating neural networks. The bad news is the API is not supported by any of the inference engines (afaik), but the NPU can run models directly that have been converted to the RKNN format. It is a long shot, but you can find details here.
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MMDeploy: Deploy All the Algorithms of OpenMMLab
BibTeX @misc{=mmdeploy, title={OpenMMLab's Model Deployment Toolbox.}, author={MMDeploy Contributors}, howpublished = {\url{https://github.com/open-mmlab/mmdeploy}}, year={2021} }
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Removing the bounding box generated by OnnxRuntime segmentation
I have a semantic segmentation model trained using the mmdetection repo. Then it is converted to the ONNX format using the mmdeploy repo.
What are some alternatives?
ByteTrack - [ECCV 2022] ByteTrack: Multi-Object Tracking by Associating Every Detection Box
FastDeploy - ⚡️An Easy-to-use and Fast Deep Learning Model Deployment Toolkit for ☁️Cloud 📱Mobile and 📹Edge. Including Image, Video, Text and Audio 20+ main stream scenarios and 150+ SOTA models with end-to-end optimization, multi-platform and multi-framework support.
FastMOT - High-performance multiple object tracking based on YOLO, Deep SORT, and KLT 🚀
mmflow - OpenMMLab optical flow toolbox and benchmark
PaddleDetection - Object Detection toolkit based on PaddlePaddle. It supports object detection, instance segmentation, multiple object tracking and real-time multi-person keypoint detection.
mmfewshot - OpenMMLab FewShot Learning Toolbox and Benchmark
norfair - Lightweight Python library for adding real-time multi-object tracking to any detector.
mmdetection - OpenMMLab Detection Toolbox and Benchmark
Yolov7_StrongSORT_OSNet - Real-time multi-camera multi-object tracker using YOLOv7 and StrongSORT with OSNet
mmpretrain - OpenMMLab Pre-training Toolbox and Benchmark
mmyolo - OpenMMLab YOLO series toolbox and benchmark. Implemented RTMDet, RTMDet-Rotated,YOLOv5, YOLOv6, YOLOv7, YOLOv8,YOLOX, PPYOLOE, etc.
mmrotate - OpenMMLab Rotated Object Detection Toolbox and Benchmark