Deep-Learning | R-CNN : Regions with Convolutional Neural Network | Machine Learning library

I am trying to create a GPU instance (n1-standard-2 with 1 NVIDIA T4 GPU) on Compute Engine and I have been getting this error since yesterday:

Sorry if my description is long and boring but I want to give you most important details to solve my problem. Recently I bought a Jetson Nano Developer Kit with 4Gb of RAM, finally!, and in order to get, which I consider, the best configuration for object detection I am following this guide made by Adrian Rosebrock from Pyimagesearch:

https://www.pyimagesearch.com/2020/03/25/how-to-configure-your-nvidia-jetson-nano-for-computer-vision-and-deep-learning/ Date:March, 2020. A summary of this guide is the following:

• 1: Flash Jetson Pack 4.2 .img inside a microSD for Jetson Nano(mine is 32GB 'A' Class)
• 2: Once inserted on the Nano board, configure Ubuntu 18.04 and get rid of Libreoffice entirely to get more available space
• 3: Step #5: Install system-level dependencies( Including cmake, python3, and nano editor)
• 4: Update CMake (without any errors)
• 5: Install OpenCV system-level dependencies and other development dependencies
• 6: Set up Python virtual environments on your Jetson Nano( succesfully installed virtualenv and virtualenvwrapper without errors including the bash file edition with nano)
• 7: Create virtaul env with python 3 and install protobuf and libprotobuf to get an more efficient Tensorflow. Succesfully installed. It took an hour to finish, that's normal
• 8: Here comes the headbreaker: install numpy and cython inside this env and check it importing numpy library When I try to do this step I get: Illegal instruction(core dumped) as you can see in the image: [Error with Python3.6.9]: https://i.stack.imgur.com/rAZhm.png

I said, well let's continue with this tutorial anyway:

• 9: Install Scipy v1.3.3: everything is ok with first three lines, but when I have to use python to execute the stup.py file, IT shows up again(not the clown). [Can't execute this line either]: https://i.stack.imgur.com/wFmnt.jpg

Then I ran an experiment, I have created this "p2cv4" env with Python 2, installed numpy and tested it: [With Python 2]: https://i.stack.imgur.com/zCWif.png

I can exit() whenever I want and execute other lines that use python So I concluded that is a python version issue. When I want to execute any python code, terminal ends the program with core dumping, apt-get or pip DO NOT show any errors. And I want to use python 3 because someday in the future a package or library will require python 3.

For python 3 last version for the Jetson Nano is 3.6.9, and idk which version was currently active in March, 2020, like the one Adrian used at that time

In other posts I read that this SIGILL appears when a package or library version like Numpy of TF is not friendly anymore with a specific old or low power CPU, like in this posts: Illegal hardware instruction when trying to import tensorflow, https://github.com/numpy/numpy/issues/9532

So I want to downgrade to a older python version like 3.6.5 or 3.5 but I can't find clear steps to do so in Ubuntu. I thinks this will fix this error and let me continue with configurations on the Jetson Nano.

The pyimageseach guide uses Python 3.6 but it do not specifies if is last 3.6.9 or another. If is not python causing this error let me know. HELP please!

In Python you can use a pretrained model as a layer as shown below (source here)

I need to use face detection to finish my homework and then I searched on the Internet and I think that using a pre-trained deep learning face detector model with OpenCV's DNN module is easy and good, it works well. Where I learnt it is here: https://www.pyimagesearch.com/2018/02/26/face-detection-with-opencv-and-deep-learning/ , but I am really confused about the 4D array returned by net.forward():

I have this code in which mtcnn detects faces on an image, draws a red rectangle around each face and prints on the screen.

Code taken from: https://machinelearningmastery.com/how-to-perform-face-detection-with-classical-and-deep-learning-methods-in-python-with-keras/

But I want to save the image with the red boxes arround each face. So that i can do some preprocessing on it. Any help is good.

I want to do the same as F. Chollet's notebook but in C#.

However, I can't find a way to iterate over my KerasIterator object:

I'm trying to "convert" the Keras notebooks made by F. Chollet to C# / .NET applications. You can find them here. I am specifically working on "3.5 - Movie Reviews" as of right now.

The problem is, I can't convert my NDarrays to C# arrays to use the values. I tried this method (in README - section Performance Considerations), but I get random values or Python Runtime errors.

I am trying to find the direction of triangles in an image. below is the image:

These triangles are pointing upward/downward/leftward/rightward. This is not the actual image. I have already used canny edge detection to find edges then contours and then the dilated image is shown below.

My logic to find the direction:

The logic I am thinking to use is that among the three corner coordinates If I can identify the base coordinates of the triangle (having the same abscissa or ordinates values coordinates), I can make a base vector. Then angle between unit vectors and base vectors can be used to identify the direction. But this method can only determine if it is up/down or left/right but cannot differentiate between up and down or right and left. I tried to find the corners using cv2.goodFeaturesToTrack but as I know it's giving only the 3 most effective points in the entire image. So I am wondering if there is other way to find the direction of triangles.

Here is my code in python to differentiate between the triangle/square and circle:

I'm working on a deep learning project and I tried following a tutorial to evaluate my model with Cross-Validation.

I was looking at this tutorial: https://machinelearningmastery.com/use-keras-deep-learning-models-scikit-learn-python/

I started by first splitting my dataset into features and labels: