YOLO | implement the YOLO algorithm using MXNet/Gluon | Machine Learning library
kandi X-RAY | YOLO Summary
kandi X-RAY | YOLO Summary
implement the YOLO algorithm using MXNet/Gluon. Only support the toy dataset for now.
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Top functions reviewed by kandi - BETA
- Compute the classification loss
- Implementation of IOU2
- Return label and location locations
- Split y into label label
- Calculate the confidence interval loss
- Translate labels to labels
- Finds a grid in a given location
- Translate locat to local coordinates
YOLO Key Features
YOLO Examples and Code Snippets
Community Discussions
Trending Discussions on YOLO
QUESTION
I have two large-ish data frames I am trying to append...
In df1, I have state codes, county codes, state names (Alabama, Alaska, etc.), county names, and years from 2010:2020.
In df2, I have county names, state abbreviations (AL, AK), and data for the year 2010 (which I am trying to merge into df1. The issue lies in that without specifying the state name and simply merging df1 and df2, some of the data which I am trying to get into df1 is duplicated due to there being some counties with the same name...hence, I am trying to also join by state to prevent this, but I have state abbreviations, and state names.
Is there any way in which I can make either the state names in df1 abbreviations, or the state names in df2 full names? Please let me know! Thank you for the help.
Edit: dput(df2)
...ANSWER
Answered 2022-Apr-18 at 03:52Here's one way you could turn state abbreviations into state names using R's built in state vectors:
QUESTION
Gonna ask a newbie question...
If I finished training my model, or I am using trained model like YOLO. And I want to put the model on a robot that has a 6GB VRAM. In this case, do I need to concern about the batch size at all?
I am trying to find out if models like YOLO will fit in my GPU. Thank you
...ANSWER
Answered 2022-Apr-11 at 17:09Generally speaking, batch size can be adjusted at any time without creating a problem. Each element of a batch is independent, but they are fed through the network together for efficiency reasons.
Note that batch size does affect training quality, as the gradients from larger batches will average out and have less variance. But that is irrelevant when doing inference (actually using the model).
You also asked what prevents you deploying a huge model on a small GPU, and the answer is simply performance. It is entirely possible to load part of a large model onto the GPU, run that part, load the next part, run it, and so on. You would need to balance the batch size and model part size, because if you only use a batch size of 1, the continual copying of model parameters will probably make it slower than running the whole model on the CPU.
QUESTION
What does the code mean, and how to invoke it?
...ANSWER
Answered 2022-Mar-05 at 03:43It's an Anonymous function.
The usual way to use them is to either assign it to a variable, which would become the function's name:
QUESTION
I have created a map of multiple different data types. s64, f64, Arrays, Images, etc.
To do so, i used a map of type std::map> database;
.
I want to store it, and reload it from filesystem. But i heard that maps cant be sored in a one-liner. So i tried to store the data part std::unique_ptr test;
of one pair first:
ANSWER
Answered 2022-Feb-28 at 15:10I spent a large amount of time making things self-contained. Among the many changes:
you should not have the base class serializing the derived (that's classic OOP), instead Boost expects derived classes to serialize their
base_object<>
(allowing static polymorphism and, incidentally, type registration)of course, the base class should serialize its data members (
type
)the base class SHOULD have a virtual destructor (otherwise deleting through
unique_ptr
's destructor will be unspecifiedUp till here:
QUESTION
You'll need this notebook to reproduce the error which downloads the files below and runs the exact same code following the description.
labels.csv
: each row containsx0
,y0
,x1
,y1
text coordinates, and other columns not affecting the outcome.yolo-train-0.tfrecord
: Contains 90% of the examples found inlabels.csv
. Each example contains all labels/rows corresponding to the image in the example.
I'm experiencing a recurring error that happens when iterating over a tfrecord dataset. After 2000-4000 iterations that successfully read batches from the dataset, I get the following error:
...ANSWER
Answered 2022-Feb-14 at 16:16Wrapping the transform_targets_for_output
method with a try-except-raise
clause and applying tf.data.experimental.ignore_errors
to the dataset seems to actually work:
QUESTION
I am taking an uploaded image from the user and then sending it to a YOLO model which then returns me an image.
I want to store that returned image in my Local Directory and then display it on the user interface.
This is the Code of views.py
that takes in an image and sends it to the Yolo Model,
ANSWER
Answered 2022-Feb-13 at 16:57You can use the imwrite
function of cv2
library to store your files in the local directory, i.e.,
In your case, simply do this,
QUESTION
ANSWER
Answered 2021-Nov-17 at 18:39There is no direct way to convert the normalized Yolo format to another format like Pascal VOC, because you need to know the size of the image to do the conversion. (Just like you need to know the size of the image to convert it to the normalized yolo format in the first place.) You will also want to provide some mapping to convert the class numbers to class names.
I am working on a Python package to simplify these kinds of conversions called PyLabel. I have a sample notebook that will convert Yolo v5 annotations to VOC format here https://github.com/pylabel-project/samples/blob/main/yolo2voc.ipynb. You can open it in Colab using this link.
The core code will be something like this:
QUESTION
Yolov5 doesn't support segmentation labels and I need to convert it into the correct format.
How would you convert this to yolo format?
...ANSWER
Answered 2021-Dec-21 at 00:34After our back and forth in the comments I have enough info to answer your question. This is output from the Google Vision API. The normalizedVertices are similar to the YOLO format, because they are "normalized" meaning the coordinates are scaled between 0 and 1 as opposed to being pixels from 1 to n. Still, you need to do some transformation to put into the YOLO format. In the YOLO format, the X and Y values in the 2nd and 3rd columns refer to the center of the bounding box, as opposed to one of the corners.
Here is a code snipped that will sample at https://ghostbin.com/hOoaz/raw into the follow string in YOLO format '0 0.5080664305 0.5624289849999999 0.9786587390000001 0.56914843'
QUESTION
I am trying to run this github repo found at this link: https://github.com/HowieMa/DeepSORT_YOLOv5_Pytorch After installing the requirements via pip install -r requirements.txt. I am running this in a python 3.8 virtual environment, on a dji manifold 2g which runs on an Nvidia jetson tx2.
The following is the terminal output.
...ANSWER
Answered 2021-Nov-11 at 05:39Try this:
QUESTION
I wanted to know the number of vehicles in the picture using yolov5 However, the result of the model was different from detect.py
0. img
1. model_result
...ANSWER
Answered 2021-Oct-24 at 21:48It seems it is an image processing issue.
Indeed with your example, the model loaded from torch hub gives a different output than detect.py
. Looking at the source code of detect.py
I see that there is some good image pre-processing. From the model hub, I really don't know what's happening to the input. From the model hub, the resulting image is this:
With their pre-processing, this is basically the image that you are feeding into the model. Would not expect any detections from this to be honest.
But then I tried doing the pre-processing myself (noted as well in their tutorial)
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Install YOLO
You can use YOLO like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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