<p>It is much better to know basic mechanics of convnets first ,rather than diving straight into complicated models .</p>

<p><em>For training data sets (the images) I need to collect, I placed a camera. By recording it and collecting images from there, I am getting images with more noise. Can I use these images for training? Also, which yolo version should I use? What are the important points that I should consider for training datasets?</em> </p>

<p>After you are good with the theory part most of your questions will be answered , otherwise you would endup with nothing but buzzwords.</p>

<p><em>I want to use any version of yolo compatible with tensorflow.</em></p>

<p>Tensorflow is a framework for building neural networks , so in theory you can build any network with it so compatibility is not at all a problem.</p>

CC BY-SA 4.0
2018-07-02T12:46:06.317Z
2018-07-03T04:26:43.007Z
2018-07-03T04:26:43.007Z
thecomplexitytheorist
<p>As long as all training samples have same type and level of noise, it shouldn't affect the outcome/accuracy.</p>
<p>As far as real time performance is concerned, you should compare the &quot;Params, FLops and Inference time @ B1 (Batch size 1)&quot; numbers of different yolo versions.
Lower the number, faster the inference.</p>
<p>You can start by exploring yolov5 and Yolo-Fastestv2 at the links below:</p>
<p><a href="https://github.com/ultralytics/yolov5" rel="nofollow noreferrer">https://github.com/ultralytics/yolov5</a></p>
<p><a href="https://github.com/dog-qiuqiu/Yolo-FastestV2" rel="nofollow noreferrer">https://github.com/dog-qiuqiu/Yolo-FastestV2</a></p>
<p>Unfortunately most of the implementations are in PyTorch, but you can easily convert a PyTorch model to a Tensorflow one by first converting it to an ONNX format.</p>

CC BY-SA 4.0
2022-08-09T15:58:58.143Z
2022-08-09T15:58:58.143Z
Marib Sultan
<p>I want to detect drivers with or without seatbelts at crossroads. For that, as it is real-time, I am going to use the YOLO algorithm/model. For training data sets (the images) I need to collect, I placed a camera. By recording it and collecting images from there, I am getting images with more noise. </p>

<p>Can I use these images for training? Also, which YOLO version should I use? What are the important points that I should consider for training datasets?</p>

<p>I want to use any version of YOLO compatible with TensorFlow.</p>

<machine-learning><deep-learning><tensorflow><yolo>
What YOLO algorithm can I use for images with noise as I will implement it in real time?
ťęjă RAVURI
# What YOLO algorithm can I use for images with noise as I will implement it in real time?

(Asked by: username_0 on 2018-07-01)

I want to detect drivers with or without seatbelts at crossroads. For that, as it is real-time, I am going to use the YOLO algorithm/model. For training data sets (the images) I need to collect, I placed a camera. By recording it and collecting images from there, I am getting images with more noise.

Can I use these images for training? Also, which YOLO version should I use? What are the important points that I should consider for training datasets?

I want to use any version of YOLO compatible with TensorFlow.

**Tags:** machine-learning, deep-learning, tensorflow, yolo


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## 2 answers

### Answer by username_1 on 2018-07-02. Score: 1

It is much better to know basic mechanics of convnets first ,rather than diving straight into complicated models .

*For training data sets (the images) I need to collect, I placed a camera. By recording it and collecting images from there, I am getting images with more noise. Can I use these images for training? Also, which yolo version should I use? What are the important points that I should consider for training datasets?*

After you are good with the theory part most of your questions will be answered , otherwise you would endup with nothing but buzzwords.

*I want to use any version of yolo compatible with tensorflow.*

Tensorflow is a framework for building neural networks , so in theory you can build any network with it so compatibility is not at all a problem.


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### Answer by username_2 on 2022-08-09. Score: 0

As long as all training samples have same type and level of noise, it shouldn't affect the outcome/accuracy.

As far as real time performance is concerned, you should compare the "Params, FLops and Inference time @ B1 (Batch size 1)" numbers of different yolo versions.
Lower the number, faster the inference.

You can start by exploring yolov5 and Yolo-Fastestv2 at the links below:

<https://github.com/ultralytics/yolov5>

<https://github.com/dog-qiuqiu/Yolo-FastestV2>

Unfortunately most of the implementations are in PyTorch, but you can easily convert a PyTorch model to a Tensorflow one by first converting it to an ONNX format.


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