voc | play English vocabulary 's audio via command line

I'm trying to put a mean in a boxplot with facets but the mean points does not end up on top of the boxplot but next to them. Any suggestions as how to fix this so that the mean points end up on top of the boxplots?

Here's the code for the ggplot2

I'm looking for something that can help me with detection of eye via YOLO method. Is it even possible? Saw that COCO and pascal VOC datasets are wide but there's lack of eye class. Is there any dataset or something that could help me with such detection?

I want to add a new column called academics_category, which contains the values academic degree and no academic degree. I created a udf function who checks if a bildungsstand (education) matches to a academic degree or not.

The problem is, that every value in the output is no academic degree.

I have been having issues setting up Darknet. I will be using yolov2 to detect cerebral microbleeds for a neuroscience project. After battling Darknet for a few days, I managed to install it and successfully download the train, test and validation Pascal VOC data by using the below general configuration/set up:

• Cmake-GUI 3.2 (binary distributions, not source)
• MSVS 2019
• CUDA 11.1
• cuDNN 8.0.5
• OpenCV 4.2.0

I always get an error when running darknet.exe detector train data/voc.data yolo-voc.2.0.cfg darknet19_448.conv.23 in cmd:

'darknet.exe' is not recognized as an internal or external command, operable program or batch file

I cannot seem to understand the reason why.

In addition, when following the pjreddie instructions to modify cfg for Pascal Data:

Seeking help regarding image annotation formats for object detection API.

• Foreknow:

As, we know there are two annotation formats for images, Pascal VOC and COCO formats. Both have their own specification here's the main difference between both:

Pascal VOC:

1. Stores annotation in .xml file format.
2. Bounding box format [x-top-left, y-top-left, x-bottom-right, y-bottom-right]
3. Create separate xml annotation file for each image in the dataset.

COCO:

1. Stores annotation in .json file format.
2. Bounding box format [x-top-left, y-top-left, width, height].
3. Create one annotation file for each training, testing and validation.

• Current-issue:

I have two dataset to deal and this is how they are annotated.

Dataset-1:

• File format: Pascal VOC(.xml)
• Bounding box format: COCO.
• File creation: As in Pascal VOC(separate xml annotation file for each image in the dataset).

Dataset-2:

• File format: Pascal VOC(.xml)
• Bounding box format: COCO.
• File creation: As in COCO(Create one annotation file for each training, testing and validation)

The thing that I am not able to get pass through is which format(Pascal VOC or COCO) should I follow to convert my annotations into Tfrecords(.xml to .records) as use can see the annotations of dataset aren't purely belong to any of one format.

For instance, in this link the author wrote a script to convert .xml into .records but here it is dealing with pure pascal VOC format.

And in this link they are dealing with pure COCO annotation formats.

Which path should I follow as I am standing in the middle of both formats?

I need to do Semantic image segmentation based on Unet.

I have to work with Pascal VOC 2012 dataset, however I don't know how to do it, do I manually select images for the train & val and convert them into numpy and then load them into the model? Or is there another way?

If this is the first one I would like to know how to convert all the images present in a folder into .npy.

I am working on running this code in Python:

I'm trying to list all the wrong predictions in a test set, but quite unsure how to do it. I tried Stackoverflow, but might have searched for the wrong "problem". So I have these text files from a folder, containing emails. The problems is that my predictions isn't doing to well, and I want to inspect the emails that is predicted wrong. Currently a snippet of my code looks something like this:

I'm a beginner in Machine Learning.
I've been learning about YOLO and DarkFlow from the following links with Ubuntu 20.04: darkflow and Tiny YOLO.

I successfully executed the code, and got the results like this:

Statistics:
car: 436
person: 73
Dataset size: 2599
Dataset of 2599 instance(s)
Training statistics:
Learning rate : 1e-05
Batch size : 16
Epoch number : 1000
Backup every : 2000

It's not bad, but the training's taking way too much time.
But I wanna know if there is any powerful IDE or other tools that can help me reduce time.
I searched in google and tried to find many ways to improve.
I heard that there are many ways to make training faster,(including Azure AI ML service) but since I'm a beginner, I cannot decide which will be the perfect choice to run YOLO and DarkFlow.

I would appreciate advices about robust development environments, especially the ones that would be suitable in my current specific condition.
Thanks in advance!

++) Since I'm a mere sophomore, the level of the hardware that I can use is very limited. I would also appreciate tools that can help me overcome the limitations of my hardware!