Week1&2&3: Introduction & Setup JetsonNano
Sumary
- Setting up Jetson with JetPack
- Running the Docker Container
- The containers use the l4t-pytorch base container, so support for transfer learning / re-training is already included
- How to connect CSI Camera to the Jetson-Nano can be followed
Assigments
- All the assigments related to the setup of Jetson-Nano should be sent to professor by email. As a Subject write "[IoT-DA Edge] Assignments Weeks1&2&3"
Assign 1
- Image classification using the imagenet script with an input example images/strawberry_0.jpg
Assign 2
- Test Pedestrian Detection example running the detectnet script with an input example data/images/peds_3.jpg
- Download test video with the next command and test *detectnet with pedestrians as output
root@jetson-nano:/jetson-inference/build/aarch64/bin# wget https://nvidia.box.com/shared/static/veuuimq6pwvd62p9fresqhrrmfqz0e2f.mp4 -O images/pedestrians.mp4
root@jetson-nano:/jetson-inference/build/aarch64/bin# ./detectnet images/pedestrians.mp4 images/test/pedestrians.mp4
Assign 3
- Create a Jupyter Notebook that
- Prints on the screen "Hello World" with the python command print. More info about how to use print could be found in the link
- Given to vectors
x = [5, 10, -5, 6, 9]andy = [4, -7, -1, 0.5, 8]write a for-loop that compute the dot product of x and y