NanoDet with tracking for a bare Raspberry Pi 4 using ncnn.
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Updated
Nov 6, 2023 - C++
NanoDet with tracking for a bare Raspberry Pi 4 using ncnn.
Implemented the prediction inference process of the NANODET model in ONNX format and TFLite format
Navigation software for autonomous robot. Real-time object detection using high-performance neural network inference computing framework. To communicate with microcontroller, text-based bluetooth communication. Progress of the roboter is shown on Website: We communicate with the server using websockets https://github.com/cyrillkuettel/rover/
Nanodet, NanodetPlus, Yolov5, Yolov6, Yolov7, MobileSSD etc. deployment with ncnn/dnn/mnn/SNPE/mace/Torch onto Android
Some Nanodet trained models
NanoDet for a bare Raspberry Pi 4
NanoDetをGoogle Colaboratory上で訓練しONNX形式のファイルをエクスポートするサンプル(This is a sample to training NanoDet on Google Colaboratory and export a file in ONNX format)
This repo is implemented based on detectron2
A collection of some awesome public Anchor-Free object detection series projects.
NanoDetのPythonでのONNX推論サンプル
NanoDet: Tiny Object Detection for TFJS and NodeJS
docker images for training, mining and infer for ymir
🍅🍅NanoDet、NanoDet-Plus with ONNXRuntime/MNN/TNN/NCNN C++. (https://github.com/DefTruth/lite.ai.toolkit)
Tracking-by-Detection形式のMOT(Multi Object Tracking)について、 DetectionとTrackingの処理を分離して寄せ集めたフレームワーク(Tracking-by-Detection method MOT(Multi Object Tracking) is a framework that separates the processing of Detection and Tracking.)
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