title: CNN: Is it better to train 300.000 images during 1 epoch or 300 images during 1000 epoch?
body: <p>This question is related to convolutionals neural networks (especially YoloV3)</p>
  
  <p>Since one epoch is one forward pass and one backward pass of all the training examples, for the model to converge properly, is it the same (in terms of precision and time to converge) to :</p>
  
  <ul>
  <li>train with <strong>n*k images</strong> during <strong>m epochs</strong> ?</li>
  <li>train with <strong>n images</strong> during <strong>m*k epochs</strong> ?</li>
  </ul>