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marwan1023 / repository
Leverage the Intel® Distribution of OpenVINO™ Toolkit to fast-track development of high-performance computer vision and deep learning inference applications, and run pre-trained deep learning models for computer vision on-premise. You will identify key hardware specifications of various hardware types (CPU, VPU, FPGA, and Integrated GPU), and utilize the Intel® DevCloud for the Edge to test model performance on the various hardware types. Finally, you will use software tools to optimize deep learning models to improve performance of Edge AI systems. - Source
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The project aims to create a people counting smart camera able to detect people using an optimized AI model at the Edge and extract relevant statistics like:
These statistics are sent using JSON and MQTT to a server, for bandwidth saving enabling the use of the low-speed link. If needed is always possible to watch remotely the video stream for seeing what's is currently happening.
The challenges in this project are: select the right pre-trained model for doing the object detection, optimize the model to allow the inference on low-performance devices, properly adjust the input video stream using OpenCV for maximizing the model accuracy.
| Details | |
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| Programming Language: | Python 3.5 or 3.6 |
The goal of this project is building an application to reduce congestion and queuing systems.
sudo /opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/downloader.py --name person-detection-retail-0013
Manufacturing Sector
| CPU | FPGA | GPU | VPU |
|---|---|---|---|
Retail Sector
| CPU | FPGA | GPU | VPU |
|---|---|---|---|
Transportation Sector
| CPU | FPGA | GPU | VPU |
|---|---|---|---|
In this project, you will use a Gaze Detection Model Gaze Detection Model to control the mouse pointer of your computer.
You will be using the Gaze Estimation model to estimate the gaze of the user's eyes and change the mouse pointer position accordingly.
This project will demonstrate your ability to run multiple models in the same machine and coordinate the flow of data between those models.
The gaze estimation model requires three inputs you will have to use three other OpenVino models:
The head pose The left eye image The right eye image.
To get these inputs, you will have to use three other OpenVino models:
You will have to coordinate the flow of data from the input, and then amongst the different models and finally to the mouse controller. The flow of data will look like this:
I ran the model inference on CPU and GPU device on local machine given same input video and same virtual environment. Listed below are hardware versions: Model precisions tested:
FP32 FP16 INT8 Hardwares tested:
CPU (2.3 GHz Intel Core i5) GPU (Intel(R) UHD Graphics 630)
I have checked Inference Time, Model Loading Time, and Frames Per Second model for FP16, FP32, and FP32-INT8
Benchmark results of the model. CPU(FP32-INT8,FP16,FP32) and Asynchronous Inference
Benchmark results of the model. GPU(FP32-INT8,FP16,FP32) and Asynchronous Inference
Due to non availability of FPGA and VPU in local machine, I did not run inference for these device types.
FP32
| Type of Hardware | Total inference time | Total load time | fps |
|---|---|---|---|
| CPU | 31.6s | 0.930308s | 1.867089 |
| GPU | 32.8s | 33.834617s | 1.798780 |
FP16
| Type of Hardware | Total inference time | Total load time | fps |
|---|---|---|---|
| CPU | 31.8s | 1.165073s | 1.855346 |
| GPU | 32.6s | 34.921903s | 1.809816 |
FP32-INT8
| Type of Hardware | Total inference time | Total load time | fps |
|---|---|---|---|
| CPU | 32.0s | 2.662999s | 1.843750 |
| GPU | 34.1s | 47.700375s | 1.730205 |
And more sources, see this link. The Free Foundation course is from Udacity and Intel| Intel® Edge AI Fundamentals with OpenVINO™