Answers.Body: <p>I think you underestimate the size of YOLO. This is the size of one segment of yolo tiny according to the darknet .cfg file:</p>
  
  <pre><code>Convolutional Neural Network structure:
  416x416x3                Input image
  416x416x16               Convolutional layer: 3x3x16, stride = 1, padding = 1
  208x208x16               Max pooling layer: 2x2, stride = 2
  208x208x32               Convolutional layer: 3x3x32, stride = 1, padding = 1
  104x104x32               Max pooling layer: 2x2, stride = 2
  104x104x64               Convolutional layer: 3x3x64, stride = 1, padding = 1
  52x52x64                 Max pooling layer: 2x2, stride = 2
  52x52x128                Convolutional layer: 3x3x128, stride = 1, padding = 1
  26x26x128                Max pooling layer: 2x2, stride = 2
  26x26x256                Convolutional layer: 3x3x256, stride = 1, padding = 1
  13x13x256                Max pooling layer: 2x2, stride = 2
  13x13x512                Convolutional layer: 3x3x512, stride = 1, padding = 1
  12x12x512                Max pooling layer: 2x2, stride = 1
  12x12x1024               Convolutional layer: 3x3x1024, stride = 1, padding = 1
  </code></pre>
  
  <p>.cfg file found here: <a href="https://github.com/pjreddie/darknet/blob/master/cfg/yolov3-tiny.cfg" rel="nofollow noreferrer">https://github.com/pjreddie/darknet/blob/master/cfg/yolov3-tiny.cfg</a></p>
  
  <p>EDIT: These networks generally aren't specifically designed to train fast, they're designed to run fast at test time, where it matters</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-02-28T11:08:14.240Z
Answers.Text: yeah, that tiny network is still huge
Answers.UserDisplayName: Dan D.
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-02-28T05:21:09.443Z
Answers.LastActivityDate: 2020-02-28T05:21:09.443Z
Answers.UserDisplayName: Recessive
Answers.Body: <p>It depends upon the factors such as 
  1. Batch size (GPU memory capacity)
  2. CPU speed and number of cores(multi-threading to load the images)</p>
  
  <p>Number of classes increase the number of convolution filters only in the prediction layers of YOLO. It influences only less than 1% speed of the detector to train the model.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-02-28T11:08:55.520Z
Answers.Text: u said it right, i train now on gpu and it's 10x to 100x faster
Answers.UserDisplayName: Dan D.
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-02-28T06:08:50.547Z
Answers.LastActivityDate: 2020-02-28T06:08:50.547Z
Answers.UserDisplayName: Sudhakar17
Body: <p>I'm testing out YOLOv3 using the 'darknet' binary, and custom config. It trains rather slow.</p>
  
  <p>My testing out is only with 1 image, 1 class, and using YOLOv3-tiny instead of YOLOv3 full, but the training of yolov3-tiny isn't fast as expected for 1 class/1 image.</p>
  
  <p>The accuracy reached near 100% after like 3000 or 4000 batches, in similarly 3 to 4 hours. </p>
  
  <p><strong>Why is it slow with just 1 class/1 image?</strong></p>
  
Tags: <training><computer-vision><object-detection><yolo><darknet>
Title: What are the reasons behind slow YOLO training?
UserDisplayName: Dan D.
ThreadText: # What are the reasons behind slow YOLO training?
  
  (Asked by: username_0 on 2020-02-28)
  
  I'm testing out YOLOv3 using the 'darknet' binary, and custom config. It trains rather slow.
  
  My testing out is only with 1 image, 1 class, and using YOLOv3-tiny instead of YOLOv3 full, but the training of yolov3-tiny isn't fast as expected for 1 class/1 image.
  
  The accuracy reached near 100% after like 3000 or 4000 batches, in similarly 3 to 4 hours.
  
  **Why is it slow with just 1 class/1 image?**
  
  **Tags:** training, computer-vision, object-detection, yolo, darknet
  
  ---
  
  ## 2 answers
  
  ### Answer by username_1 on 2020-02-28. Score: 2 | Accepted answer
  
  I think you underestimate the size of YOLO. This is the size of one segment of yolo tiny according to the darknet .cfg file:
  Convolutional Neural Network structure:
  416x416x3                Input image
  416x416x16               Convolutional layer: 3x3x16, stride = 1, padding = 1
  208x208x16               Max pooling layer: 2x2, stride = 2
  208x208x32               Convolutional layer: 3x3x32, stride = 1, padding = 1
  104x104x32               Max pooling layer: 2x2, stride = 2
  104x104x64               Convolutional layer: 3x3x64, stride = 1, padding = 1
  52x52x64                 Max pooling layer: 2x2, stride = 2
  52x52x128                Convolutional layer: 3x3x128, stride = 1, padding = 1
  26x26x128                Max pooling layer: 2x2, stride = 2
  26x26x256                Convolutional layer: 3x3x256, stride = 1, padding = 1
  13x13x256                Max pooling layer: 2x2, stride = 2
  13x13x512                Convolutional layer: 3x3x512, stride = 1, padding = 1
  12x12x512                Max pooling layer: 2x2, stride = 1
  12x12x1024               Convolutional layer: 3x3x1024, stride = 1, padding = 1
  
  .cfg file found here: https://github.com/pjreddie/darknet/blob/master/cfg/yolov3-tiny.cfg
  EDIT: These networks generally aren't specifically designed to train fast, they're designed to run fast at test time, where it matters
  
  > **Comments:**
  > **username_0**: yeah, that tiny network is still huge  
  
  ---
  
  ### Answer by username_2 on 2020-02-28. Score: 1
  
  It depends upon the factors such as
  1. Batch size (GPU memory capacity)
  2. CPU speed and number of cores(multi-threading to load the images)
  
  Number of classes increase the number of convolution filters only in the prediction layers of YOLO. It influences only less than 1% speed of the detector to train the model.
  
  > **Comments:**
  > **username_0**: u said it right, i train now on gpu and it's 10x to 100x faster  
  
  ---
  
  