You'll learn how to:
-Set up your Jetson Nano and (optional) camera
-Build end-to-end DeepStream pipelines to convert raw video input into insightful annotated video output
-Configure multiple video streams simultaneously
注意，虽然课程是基于Jetson NANO的，其实Xavier和Jetson TX2都是支持的哟！
Jetson Nano Developer Kit
Computer with Internet Access and SD card port
A microSD Memory Card (32GB UHS-I minimum)
Compatible 5V 4A Power Supply with 2.1mm DC barrel connector
USB cable (Micro-B to Type-A)
A computer with an Internet connection and:
-The ability to flash your microSD card
-Administrative rights and the ability to install a compatible media player software such as VLC Player
OPTIONAL: Compatible camera such as a Logitech C270 Webcam
OPTIONAL: Wired Internet connection to the Jetson Nano (Ethernet port)
简单地说，就是你需要有一套Jetson NANO开发套件，上面有TF卡，至少32GB，刷好系统，有5V4A电源，有跳线帽；准备一台联网的电脑，电脑上装一个媒体播放器，比如VLC；可以准备一个USB摄像头哦，比如罗技C270.（实际上跟我们购买Jetson NANO套件的套餐，会包含32G刷好系统的TF卡和5V4A电源。升级版本的Jetson NANO是自带跳线帽的,
1. Which of the following statements are true about "bounding boxes" in the context of object detection? (Check all that apply)。
- Bounding boxes are used to show target locations.
-A bounding box is a rectangular box determined with x and y coordinates of the axis.
-A bounding box is used to shrink the size of the overall image.
-In a DeepStream pipeline, the Gst-nvdsosd plugin is used to draw bounding boxes, text, and region-of-interest (RoI) polygons.
2.What is the Gst-nvinfer plugin used for? (Check all that apply)
-Performs transforms (format conversion and scaling), on the input frame based on network requirements, and passes the transformed data to the low-level library
-Performs inferencing on input data using NVIDIA TensorRT
-Sends UDP packets to the network.
-Tracks object between frames.
3.What feature describes a "hardware-accelerated" plugin?
-Interacts with hardware to deliver maximum performance such as GPU, DLA, PVA
-Improves High level software interactions such as Python or Java
-Executes on embedded processors
4.Looking at the config file on your Jetson Nano at “/home/dlinano/deepstream_sdk_v4.0.2_jetson/sources/apps/dli_apps/deepstream-test1-rtsp_out/dstest1_pgie_config.txt”: What can we understand about the model we are using for object detection and counting? (Hint: check out the "model-file", "num-detected-classes", and "output-blob-names" keys)
-ResNet-10, Number of Classes 4, Output Layer: conv2d_bbox
-AlexNet, Number of Classes 100, Output Layer: conv2d_bbox
-ResNet-10, Number of Classes 100, Output Layer: conv2d_bbox
-AlexNet, Number of Classes 1000, Output Layer: conv2d_bbox
5.Looking at the “C” file at “/home/dlinano/deepstream_sdk_v4.0.2_jetson/sources/apps/dli_apps/deepstream-test1-rtsp_out/deepstream_test1_app.c”: In line 67, we use the “NvDsBatchMeta”, metadata structure. Why is it needed? (Check all that apply)
-It is not actually used.
-We need a metadata structure to hold frames, object, classifier, and label data.
-We need to access the metadata in this structure to determine how many objects are in each frame and display them
-It is a piece of legacy code carried forward from a previous version of DeepStream.
6.What type(s) of network(s) are supported by Gst-nvinfer?
-Multi-class object detection only
-Multi-label classification only
-Multi-class object detection, multi-label classification, and segmentation
7.What is Gst-nvstreammux used for? (Check all that apply)
-It forms a batch of frames from one or multiple input sources
-It collects an average of ( batch-size/num-source ) frames per batch from each source
-It runs inference
-It tracks objects between frames
8.How should we determine the batch size for multiple stream inputs?
-It should be equal to or proportional to the number of input streams
-Batch size is inversely proportional to number of streams
-Batch size can be 1 even for multiple stream inputs
9.Which of the following plugin lists are included in both Notebook #2: "Multiple Networks Application" and Notebook #3: "Multiple Stream Inputs"?
-Gst-nvinfer (for deep learning inference), Gst-nvstreammux (for batching video streams), Gst-nvtracker (tracks object between frames)
-Gst-nvinfer (for deep learning inference), Gst-nvdsosd (for drawing bounding boxes), Gst-nvvideoconvert (for video format conversion)
-Gst-nvinfer (for deep learning inference), Gst-nvmultistreamtiler (composites a 2D tile from batched buffers), Gst-nvtracker (tracks object between frames)
-Gst-nvvideoconvert (for video format conversion), Gst-nvstreammux (for batching video streams), Gst-nvmultistreamtiler (composites a 2D tile from batched buffers)
10.What are the DeepStream supported object detection networks? (Check all that apply)
11.What can be understood by looking at the config file “/home/dlinano/deepstream_sdk_v4.0.2_jetson/sources/apps/dli_apps/deepstream-test3-mp4_out-yolo/dstest3_pgie_config_yolov3_tiny.txt”?
-Neural Network Model : YOLO-V3, Number of Classes: 4, Network Mode= FP32 (Floating Point Computations), Input to model in format: BGR
-Neural Network Model : Tiny-YOLO-V3, Number of Classes: 80, Network Mode= FP32 (Floating Point Computations), Input to model in format: RGB
-Neural Network Model : Tiny-YOLO-V3, Number of Classes: 80, Network Mode= INT8 (Floating Point Computations), Input to model in format: BGR
12.Which of the following DeepStream plugins are part of the Primary Detector -> Object Tracker -> Secondary Classifier(s) sequence used in the pipeline for Multiple Network Applications in Notebook #2? (Check all that apply):
13.What kind of video container (file type) can a DeepStream video output be saved to? (Check all that apply):
-anything GStreamer supports
14.Which of the following statements are true about DeepStream SDK? (Check all that apply)
-DeepStream SDK is based on the GStreamer framework
-DeepStream SDK is not designed to optimize performance
-DeepStream SDK is supported on systems that contain NVIDIA Jetson modules, and NVIDIA dGPU adapters.
-DeepStream SDK has a plugin interface for TensorRT for inferencing deep learning networks.
15.Which of the following are possible use cases of DeepStream? (Check all that apply)
-Intelligent Video Analytics
-AI-based video and image understanding
-Cloud-based offline processing
- 新人入手Jetson NANO如何开始学习机器视觉应用
- 为什么Jetson NANO刷几次都失败？
- Deepstream Python APP
- 新一代Jetson NANO开发套件(B01)开箱
- 实战教程：利用NVIDIA TensorRT优化一个推荐系统
- 如何搭建一个NVIDIA JetBot小车（英文）
- Jetson TX2进行摄像头驱动的开发