mmtracking
ncnn
| mmtracking | ncnn | |
|---|---|---|
| 7 | 12 | |
| 3,382 | 19,234 | |
| 1.6% | 1.0% | |
| 1.5 | 9.4 | |
| 8 months ago | 8 days ago | |
| Python | C++ | |
| Apache License 2.0 | GNU General Public License v3.0 or later |
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
ncnn
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AMD Funded a Drop-In CUDA Implementation Built on ROCm: It's Open-Source
ncnn uses Vulkan for GPU acceleration, I've seen it used in a few projects to get AMD hardware support.
https://github.com/Tencent/ncnn
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[D] Best way to package Pytorch models as a standalone application
They're using NCNN to package the model. Have a look. https://github.com/Tencent/NCNN
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Realtime object detection android app
Hi. Here is my prefered android app for realtime objet detection: https://github.com/nihui/ncnn-android-nanodet ; https://github.com/Tencent/ncnn contains a lot of android demo app for a lot of models.
- ncnn: High-performance neural network inference framework optimized for mobile
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Esp32 tensorflow lite
ncnn home page: https://github.com/Tencent/ncnn
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MMDeploy: Deploy All the Algorithms of OpenMMLab
ncnn
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Draw Things, Stable Diffusion in your pocket, 100% offline and free
Yes, Android devices tend to have bigger RAMs, making running 1024x1024 possible (this is not possible at all on iPhones, which could peak around 5GiB memory with my current implementation, some serious engineering required to bring that down on iPhone devices). The problem is I am not sure about speed. I would likely switch to NCNN (https://github.com/Tencent/ncnn) as the backend which have a decent Vulkan computing kernel support. It is definitely a possibility and there is a path to do that.
- What’s New in TensorFlow 2.10?
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[Technical Article] OCR Upgrade
As the leading open-source inference framework in China and in the world, what we like are its almost zero cost cross-platform capability, high inference speed, and minimal deployment volume. (Project address: https://github.com/Tencent/ncnn)
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Is there a functioning neural netowork or backbone written in pure C language only?
If you’re not planning on training the neural net on an embedded device and just do inference, this might interest you: https://github.com/Tencent/ncnn
What are some alternatives?
ByteTrack - [ECCV 2022] ByteTrack: Multi-Object Tracking by Associating Every Detection Box
XNNPACK - High-efficiency floating-point neural network inference operators for mobile, server, and Web
FastMOT - High-performance multiple object tracking based on YOLO, Deep SORT, and KLT 🚀
rife-ncnn-vulkan - RIFE, Real-Time Intermediate Flow Estimation for Video Frame Interpolation implemented with ncnn library
PaddleDetection - Object Detection toolkit based on PaddlePaddle. It supports object detection, instance segmentation, multiple object tracking and real-time multi-person keypoint detection.
deepdetect - Deep Learning API and Server in C++14 support for Caffe, PyTorch,TensorRT, Dlib, NCNN, Tensorflow, XGBoost and TSNE
norfair - Lightweight Python library for adding real-time multi-object tracking to any detector.
netron - Visualizer for neural network, deep learning and machine learning models
Yolov7_StrongSORT_OSNet - Real-time multi-camera multi-object tracker using YOLOv7 and StrongSORT with OSNet
darknet - Convolutional Neural Networks
mmyolo - OpenMMLab YOLO series toolbox and benchmark. Implemented RTMDet, RTMDet-Rotated,YOLOv5, YOLOv6, YOLOv7, YOLOv8,YOLOX, PPYOLOE, etc.
RPi_64-bit_Zero-2-image - Raspberry Pi Zero 2 W 64-bit OS image with OpenCV, TensorFlow Lite and ncnn Framework.