Sanity Check: Overfitting on a small dataset

bing: <p>Hi,<br>
I am trying to do a sanity check to see whether my model is training correctly or not by overfitting on a small dataset.<br>
The results are a little bit different from what I expected. The loss is not abnormally very small and accuracy is decreasing with time. Is this an intended result to expect on overfitting on a small batch or whether there is some bug in my training?<br>
Below are my graphs for valid loss and valid accuracy, samples of which are same as of training set-<br>
<div class="lightbox-wrapper"><a class="lightbox" href="https://discuss.pytorch.org/uploads/default/original/3X/b/a/bafee93e898a80a0fd4533ea76b08ab7e52ff7e6.jpeg" data-download-href="https://discuss.pytorch.org/uploads/default/bafee93e898a80a0fd4533ea76b08ab7e52ff7e6" title="validloss"><img src="https://discuss.pytorch.org/uploads/default/optimized/3X/b/a/bafee93e898a80a0fd4533ea76b08ab7e52ff7e6_2_690x345.jpeg" alt="validloss" data-base62-sha1="qGeX9kchuQW7r1jj07uqFLum0Cy" width="690" height="345" srcset="https://discuss.pytorch.org/uploads/default/optimized/3X/b/a/bafee93e898a80a0fd4533ea76b08ab7e52ff7e6_2_690x345.jpeg, https://discuss.pytorch.org/uploads/default/optimized/3X/b/a/bafee93e898a80a0fd4533ea76b08ab7e52ff7e6_2_1035x517.jpeg 1.5x, https://discuss.pytorch.org/uploads/default/optimized/3X/b/a/bafee93e898a80a0fd4533ea76b08ab7e52ff7e6_2_1380x690.jpeg 2x" data-dominant-color="F9FBFD"><div class="meta"><svg class="fa d-icon d-icon-far-image svg-icon" aria-hidden="true"><use href="#far-image"></use></svg><span class="filename">validloss</span><span class="informations">1920×960 52.7 KB</span><svg class="fa d-icon d-icon-discourse-expand svg-icon" aria-hidden="true"><use href="#discourse-expand"></use></svg></div></a></div></p>
<p><div class="lightbox-wrapper"><a class="lightbox" href="https://discuss.pytorch.org/uploads/default/original/3X/4/d/4d18b2d921980a4278b509b37fa8e729b00f2bc9.png" data-download-href="https://discuss.pytorch.org/uploads/default/4d18b2d921980a4278b509b37fa8e729b00f2bc9" title="acc"><img src="https://discuss.pytorch.org/uploads/default/optimized/3X/4/d/4d18b2d921980a4278b509b37fa8e729b00f2bc9_2_690x345.png" alt="acc" data-base62-sha1="b01GgMH4NZdgMjnoQNMJHL68vEt" width="690" height="345" srcset="https://discuss.pytorch.org/uploads/default/optimized/3X/4/d/4d18b2d921980a4278b509b37fa8e729b00f2bc9_2_690x345.png, https://discuss.pytorch.org/uploads/default/optimized/3X/4/d/4d18b2d921980a4278b509b37fa8e729b00f2bc9_2_1035x517.png 1.5x, https://discuss.pytorch.org/uploads/default/optimized/3X/4/d/4d18b2d921980a4278b509b37fa8e729b00f2bc9_2_1380x690.png 2x" data-dominant-color="FBFCFD"><div class="meta"><svg class="fa d-icon d-icon-far-image svg-icon" aria-hidden="true"><use href="#far-image"></use></svg><span class="filename">acc</span><span class="informations">4800×2400 464 KB</span><svg class="fa d-icon d-icon-discourse-expand svg-icon" aria-hidden="true"><use href="#discourse-expand"></use></svg></div></a></div></p>

eqy: <p>You might want to check that the data augmentations are the same across the training and validation set. Beyond that you might want to go down to just a single example first to check that the examples are indeed identical.</p>

bing: <p>Okay, I just turned off every augmentation and checked each sample. They are same in both training and validation set. <br> So there is a bug in my training loop, right? because accuracy don’t have to decline after every iteration and loss has to decrease?</p>

eqy: <p>That seems to be the case; you might want to see if using a simple training loop with a very simple model first gets the expected behavior.</p>