A caffe implementation of MobileNet-YOLO detection network
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Updated
Mar 28, 2021 - C++
A caffe implementation of MobileNet-YOLO detection network
🍅🍅🍅YOLOv5-Lite: Evolved from yolov5 and the size of model is only 900+kb (int8) and 1.7M (fp16). Reach 15 FPS on the Raspberry Pi 4B~
🍅 Deploy ncnn on mobile phones. Support Android and iOS. 移动端ncnn部署,支持Android与iOS。
A caffe implementation of mobilenet's depthwise convolution layer.
一个移动端跨平台的gpu+cpu并行计算的cnn框架(A mobile-side cross-platform gpu+cpu parallel computing CNN framework)
Nebula: Lightweight Neural Network Benchmarks
TensorFlow Lite classification on a bare Raspberry Pi 4 with 64-bit OS at 23 FPS
Forked from TI Repo https://git.ti.com/git/apps/tensorflow-lite-examples.git
MobileNetV2_YOLOV3 for ncnn framework
Got 100fps on TX2. Got 1000fps on GeForce GTX 1660 Ti. Implement mobilenetv1-ssd-tensorrt layer by layer using TensorRT API. If the project is useful to you, please Star it.
SmartTourism: Enhance your travel experience with image recognition on Android. Discover monument guides and categories. Create customized guides easily.
TensorFlow Lite on a bare Raspberry Pi Zero
Simple app used for detecting objects in images and videos.
TensorFlow Lite classification on a bare Raspberry Pi 4 at 33 FPS
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