Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
---|---|---|---|---|---|---|---|---|---|---|
Mobilenet Ssd Realsense | 284 | 4 years ago | 14 | mit | Python | |||||
[High Performance / MAX 30 FPS] RaspberryPi3(RaspberryPi/Raspbian Stretch) or Ubuntu + Multi Neural Compute Stick(NCS/NCS2) + RealSense D435(or USB Camera or PiCamera) + MobileNet-SSD(MobileNetSSD) + Background Multi-transparent(Simple multi-class segmentation) + FaceDetection + MultiGraph + MultiProcessing + MultiClustering | ||||||||||
Raspberrypi Facedetection Mtcnn Caffe With Motion | 198 | 7 years ago | 7 | Jupyter Notebook | ||||||
MTCNN with Motion Detection, on Raspberry Pi with Love | ||||||||||
Dd_performances | 84 | 5 years ago | Python | |||||||
DeepDetect performance sheet | ||||||||||
Mobilenet Ssd | 84 | 5 years ago | 1 | mit | Python | |||||
MobileNet-SSD(MobileNetSSD) + Neural Compute Stick(NCS) Faster than YoloV2 + Explosion speed by RaspberryPi · Multiple moving object detection with high accuracy. | ||||||||||
Keras Oneclassanomalydetection | 79 | 4 years ago | 2 | mit | Jupyter Notebook | |||||
[5 FPS - 150 FPS] Learning Deep Features for One-Class Classification (AnomalyDetection). Corresponds RaspberryPi3. Convert to Tensorflow, ONNX, Caffe, PyTorch. Implementation by Python + OpenVINO/Tensorflow Lite. | ||||||||||
Rpi_caffequery | 30 | 9 years ago | other | Python | ||||||
Caffe query framework for the Raspberry Pi | ||||||||||
Caffe | 24 | 3 years ago | n,ull | other | C++ | |||||
Caffe-ssd: a fast open framework for deep learning adapted for Raspberry Pi, Jetson Nano and Ubuntu. Fixed for cuDNN 8 | ||||||||||
Caffe Installation Raspberry Pi 3 | 20 | 7 years ago | 4 | |||||||
Ncs Pi Stream | 15 | 5 years ago | Python | |||||||
Stream video on the web from RPi with object detection using Movidius Neural Compute Stick | ||||||||||
Ssd Mobilenet Ncnn | 12 | 6 years ago | 1 | C++ | ||||||
SSD-MobileNet with Tencent ncnn framework |