alshami1

alshami1's Stars

• MYIR-TI/myir-ti-linux

  Language:C2

• beagleboard/repos-arm64

  mirror of: https://git.beagleboard.org/beagleboard/repos-arm64

  Language:Shell4

• TexasInstruments-Sandbox/edgeai-demo-monodepth-estimation

  Single camera depth estimation using MiDaS deep learning CNN and gstreamer image processing pipeline

  Language:Python4

• MostafaSayedAhmed/Linux_Image_for_BeagleBoneAI64

  1

• nmenon/k3-upstream-boot-build

  Basic bootloader build for various k3 platforms with upstream components.

  Language:Makefile68

• glennklockwood/beaglebone-ai

  Code related to experimentation with BeagleBone AI

  Language:Python4

• TexasInstruments/edgeai-studio-agent

  Edge AI Studio device agent for TI devices

  Language:Python11

• Hypnotriod/bbai64

  Small projects and experiments with the BeagleBone AI-64 platform (mostly written in Go).

  Language:Go72

• bitmingw/DM642_OUT_CH0

  Video analysis of movement on DM642 DSP board. Obsolete and refer to DM642_to_Jetson repo.

  Language:C13

• TexasInstruments/edgeai-tensorlab

  Edge AI Model Development Tools

  Language:Python285

• nameUser3210/test

  Language:Shell11

• dgatwood/VISCAPTZ

  A Raspberry Pi-based direct-motor-driving PTZ controller with VISCA support

  Language:G-code7

• dnth/yolov5-deepsparse-blogpost

  By the end of this post, you will learn how to: Train a SOTA YOLOv5 model on your own data. Sparsify the model using SparseML quantization aware training, sparse transfer learning, and one-shot quantization. Export the sparsified model and run it using the DeepSparse engine at insane speeds. P/S: The end result - YOLOv5 on CPU at 180+ FPS using on

  Language:Jupyter Notebook5413

• causemx/Diva

  Ground Control Station for Ocean Space.

  Language:C#31

• WasabiFan/tidl-yolov5-custom-model-demo

  A walkthrough and personal notes on running YOLOv5 models with TIDL.

  Language:C++286

• zhuhaijun753/ti-vision-sdk

  ti-vision-sdk

  12

• iskandarputra/Real-Time-Py-Serial-Plotter

  Real-time serial data plotting application built with PyQt5 and pyqtgraph. Displays streaming sensor values from serial device in scrolling plots.

  Language:Python2

• SV-1509/Object-Detection-on-FPGA

  Tutorial on how to run object detection on fpga

  5

• MuhammadMajidAltaf/Zynq_FPGA_Median_Filter

  Language:C1

• MuhammadMajidAltaf/Projects-object_tracking-

  object tracking (use Vivado HLS)

  Language:C++2

• cgarry3/linux_zynq

  1

• cgarry3/dcu_project

  DCU project - Bring eyes to the internet of things for real time applications

  Language:Tcl11

• cqiu2008/kcfFpgaAccelerate

  kcf tracker of FPGA Accelerate

  Language:C++106

• louisliuwei/PYNQ-CV-OV5640

  Language:VHDL3

• SamuelLBau/Pool-Shot-Tracking-using-FPGA

  The goal of Pool aid is to develop a computer vision algorithm capable of identifying pool balls on a pool table. After identifying them, Pool-Aid will recommend a potential shot that may be able to sink a ball into a pocket. An additional goal is to place this algorithm on an external processor, and accelerate certain functions using an FPGA..

  Language:C317

• tconlon03/FPGA-Object-detection

  Vivado HLS code for object detection using MOG, opening and closing operations and BLOB detection

  Language:C++52

• adamgallas/fpga_accelerator_yolov3tiny

  Language:VHDL17922

• huanggeli/yolov3tiny-ZYNQ7000

  a Real-time image recognition project with RTL accelerator and ZYNQ Architecture

  Language:VHDL306

• petrohi/tensil-zcu104-tutorial

  Language:Verilog22

• jsickafoose/portfolio

  Language:Python2