14 skills found
explosion / Lightnet🌓 Bringing pjreddie's DarkNet out of the shadows #yolo
embedeep / Free TPUFree TPU for FPGA with compiler supporting Pytorch/Caffe/Darknet/NCNN. An AI processor for using Xilinx FPGA to solve image classification, detection, and segmentation problem.
isabek / XmlToTxtImageNet file xml format to Darknet text format
EagerAI / FastaiR interface to fast.ai
tirumalnaidu / Opencl Hls Cnn AcceleratorOpenCL HLS based CNN Accelerator on Intel DE10 Nano FPGA.
nikitalpopov / Vedaivedai dataset for darknet
nirbhayph / VAMRR Vehicle DetectionDeep learning based vehicle damage detection solution. This repository is linked to the web application which is integrated with the mentioned system.
PankajJ08 / Real Time Object Detection Using YOLO V3Implementing YOLO (object detection algorithm) in PyTorch
oliverfunk / Darknet RsRust bindings for darknet
sj-on / Weapon Recognition And ClassificationAims at helping Policemen to identify a potentially dangerous situation like a person holding a deadly weapon and is trained especially for detection of the presence of GUNS in an image.
Rapternmn / Pytorch Tiny DarknetPyTorch implementation of the Tiny Darknet Image Classification algorithm
vadash / Gwent Daily DemoNow open source and free and no support and no updates ;)
Jafar-Abdollahi / Automated Detection Of COVID 19 Cases Using Deep Neural Networks With CTS ImagesThe use of advanced artificial intelligence (AI) techniques combined with radiological imaging can be useful for accurate diagnosis of the disease and can also help overcome the shortage of specialist physicians in remote villages. In this project, a new model for automatic detection of covid-19 using raw chest X-ray images is presented. The proposed model is designed to provide an accurate diagnosis for binary classification (COVID vs. pneumonia ) and multi-classification (covid, pneumonia, nodel, boronshit, normal). Our model produces 99.08% classification accuracy for binary classifications and 95.02% for multi-class cases. The DarkNet model was used in our study as a classification where you only look at the real-time object recognition system once (YOLO(v3)). We applied 17 layers of the convolution and applied different filters on each layer. Our model can be used to help radiologists discredit their initial screening and can also be used over the cloud for rapid screening of patients.
schaefferdstudio / Tiny Yolo Opencv Opencl ImplementationImplement the tiny yolo in opencv with opencl
