DataFlow | DataFlow-solution -- ICP-MS data reduction tool
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ICP-MS data reduction software.
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Trending Discussions on Data Preparation
QUESTION
I am trying to prepare the masks for image segmentation with Pytorch. I have three questions about data preparation.
What is the appropriate data format to save the binary mask in general? PNG? JPEG?
Is the mask size needed to be set square such as (224x224), not a rectangle such as (224x448)?
Is the mask value fixed when the size is converted from rectangle to square?
For example, the original mask image size is (600x900), which is binary [0,1]. However, when I applied
...ANSWER
Answered 2022-Mar-30 at 08:33- PNG, because it is lossless by design.
- It depends. More convenient is to use standard resolution, (224x224), I would start with that.
- Use resize without interpolation
transforms.Resize((300, 300), interpolation=InterpolationMode.NEAREST)
QUESTION
Please i need you help concerning my yolov5 training process for object detection!
I try to train my object detection model yolov5 for detecting small object ( scratch). For labelling my images i used roboflow, where i applied some data augmentation and some pre-processing that roboflow offers as a services. when i finish the pre-processing step and the data augmentation roboflow gives the choice for different output format, in my case it is yolov5 pytorch, and roboflow does everything for me splitting the data into training validation and test. Hence, Everything was set up as it should be for my data preparation and i got at the end the folder with data.yaml and the images with its labels, in data.yaml i put the path of my training and validation sets as i saw in the GitHub tutorial for yolov5. I followed the steps very carefully tought.
The problem is when the training start i get nan in the obj and box column as you can see in the picture bellow, that i don't know the reason why, can someone relate to that or give me any clue to find the solution please, it's my first project in computer vision.
This is what i get when the training process starts
This the last message error when the training finish
The training continue without any problem but the map and precision remains 0 all the process !!
Ps : Here is the link of tuto i followed : https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data
...ANSWER
Answered 2021-Dec-04 at 09:38Running my code in colab worked successfully and the resulats were good. I think that the problem was in my personnel laptop environment maybe the version of pytorch i was using '1.10.0+cu113', or something else ! If you have any advices to set up my environnement for yolov5 properly i would be happy to take from you guys. many Thanks again to @alexheat
QUESTION
I have written my code to create a scatter plot with a color bar on the right. But the color bar does not look right, in the sense that the color is too light to be mapped to the actual color used in the plot. I am not sure what is missing or wrong here. But I am hoping to get something similar to what's shown here: https://medium.com/@juliansteam/what-bert-topic-modelling-reveal-about-the-2021-unrest-in-south-africa-d0d15629a9b4 (about in the middle of the page)
...ANSWER
Answered 2022-Mar-24 at 22:20The colorbar uses the given alpha=.3
. In the scatterplot, many dots with the same color are superimposed, causing them to look brighter than a single dot.
One way to tackle this, is to create a ScalarMappable
object to be used by the colorbar, taking the colormap and the norm of the scatter plot (but not its alpha). Note that simply changing the alpha of the scatter object (scatter.set_alpha(1)
) would also change the plot itself.
QUESTION
When importing a .csv file, is there any way to read the data from the title of the header? Consider the .csv file in the following:
I mean, instead of start_node = round.(Int64, data[:,1])
is there another way to say "start_node" is the one in the .csv file that its header is "start node i"
ANSWER
Answered 2022-Mar-09 at 19:08The most natural way is to use CSV
along with the DataFrames
package.
Consider file:
QUESTION
I'm trying to implement a simple GAN in Pytorch. The following training code works:
...ANSWER
Answered 2022-Feb-16 at 13:43Supplying inputs in either the same batch, or separate batches, can make a difference if the model includes dependencies between different elements of the batch. By far the most common source in current deep learning models is batch normalization. As you mentioned, the discriminator does include batchnorm, so this is likely the reason for different behaviors. Here is an example. Using single numbers and a batch size of 4:
QUESTION
I am looking for a similar functionality like Fillo Excel API where we can do CRUD operations in an excel file using query like statements.
A select statement in a csv file is a great addition to the framework to provide more flexibility in test data driven approach testing.
Sample scenario: A test case that needs to have multiple data preparation of inserting records to database.
Instead of putting all test data in 1 row or 1 cell like this and do a string split before processing.
...ANSWER
Answered 2022-Feb-02 at 03:20There's no need. Karate can transform a CSV file into a JSON array in one line:
QUESTION
I have a python script that handles data transactions through sqlalchemy using:
...ANSWER
Answered 2022-Jan-31 at 06:48This is an interesting situation. It seems that maybe you can sidestep some of the manual process/thread handling and utilize something like multiprocessing's Pool. I made an example based on some other data initializing code I had. This delegates creating test data and inserting it for each of 10 "devices" to a pool of 3 processes. One caveat that seems necessary is to dispose of the engine before it is shared across fork()
, ie. before the Pool tasks are created, this is mentioned here: engine-disposal
QUESTION
I am trying to use GridSearchCV to select the best imputer strategy but I am having trouble doing that.
First, I have a data preparation pipeline for numerical and categorical columns-
...ANSWER
Answered 2022-Jan-27 at 05:26The way you specify the parameter is via a dictionary that maps the name of the estimator/transformer and name of the parameter you want to change to the parameters you want to try. If you have a pipeline or a pipeline of pipelines, the name is the names of all its parents combined with a double underscore. So for your case, it looks like
QUESTION
I'm training a model within a for
loop, because...I can.
I know there are alternative like tf.Dataset
API with generators
to stream data from disk, but my question is on the specific case of a loop.
Does TF re-initialize weights of the model at the beginning of each loop ? Or does the initialization only occurs the first time the model is instantiated ?
EDIT :
...ANSWER
Answered 2022-Jan-20 at 15:06Weights are initialized when the layers are defined (before fit
). It does not re-initialize weights afterward - even if you call fit multiple times.
To show this is the case, I plotted the decision boundary at regular training epochs (by calling fit
and then predict
):
QUESTION
I have 2 tests. I want to run the only one:
...ANSWER
Answered 2022-Jan-20 at 14:32It looks like that only something like that can resolved the issue.
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