Caffe implementation of Google MobileNet SSD detection network, with pretrained weights on VOC0712 and mAP=0.727.
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Updated
Jun 28, 2021 - Python
Caffe implementation of Google MobileNet SSD detection network, with pretrained weights on VOC0712 and mAP=0.727.
A web app for MobileNet SSD detection
Computer vision based vehicle detection and tracking using Tensorflow Object Detection API and Kalman-filtering
[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
MobileNetV3-SSD for object detection and implementation in PyTorch
MobilNet-SSD object detection in opencv 3.4.1
This project aims to do real-time object detection through a laptop cam using OpenCV. The idea is to loop over each frame of the video stream, detect objects, and bound each detection in a box.
Realtime Object recognition using OpenCV 3.3 dnn module and pretrained MobileNetSSD caffe model
A Light CNN based Method for Hand Detection and Orientation Estimation
MobileNetV3 SSD的简洁版本
Tensorflow 2 single shot multibox detector (SSD) implementation from scratch with MobileNetV2 and VGG16 backbones
Mobilenet SSD based pedestrian detection
RaspberryPi3(Raspbian Stretch) + MobileNetv2-SSDLite(Tensorflow/MobileNetv2SSDLite) + RealSense D435 + Tensorflow1.11.0 + without Neural Compute Stick(NCS)
MobileNet-SSD(MobileNetSSD) + Neural Compute Stick(NCS) Faster than YoloV2 + Explosion speed by RaspberryPi · Multiple moving object detection with high accuracy.
Spring 2018 - 10.009 Digital World 1D Project
A collection of deep learning models (PyTorch implemtation)
AR.Drone 2.0 human tracking with Mobilenet-SSD and PID control
Yet another ssd, with its runtime stack for libtorch, onnx and specialized accelerators.
Real time vehicle detection (30 FPS on intel i7-8700 CPU) using Tiny-Mobilenet V2, SSD and Receptor Field Block.
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