<p>Those are metrics printed out at every iteration of the training loop. The most important ones are the loss values, but below are basic descriptions of them all (<code>eta</code> and <code>iter</code> are self-explanatory I think).</p>
<p><code>total_loss</code>: This is a weighted sum of the following individual losses calculated during the iteration. By default, the weights are all one.</p>
<ol>
<li><p><code>loss_cls</code>: Classification loss in the ROI head. Measures the loss for box classification, i.e., how good the model is at labelling a predicted box with the correct class.</p>
</li>
<li><p><code>loss_box_reg</code>: Localisation loss in the ROI head. Measures the loss for box localisation (predicted location vs true location).</p>
</li>
<li><p><code>loss_rpn_cls</code>: Classification loss in the Region Proposal Network. Measures the &quot;objectness&quot; loss, i.e., how good the RPN is at labelling the anchor boxes as foreground or background.</p>
</li>
<li><p><code>loss_rpn_loc</code>: Localisation loss in the Region Proposal Network. Measures the loss for localisation of the predicted regions in the RPN.</p>
</li>
<li><p><code>loss_mask</code>: Mask loss in the Mask head. Measures how &quot;correct&quot; the predicted binary masks are.</p>
<p>For more details on the losses (1) and (2), take a look at the <a href="https://arxiv.org/abs/1504.08083" rel="noreferrer">Fast R-CNN paper</a> and the <a href="https://github.com/facebookresearch/detectron2/blob/cc2d218a572c2bfea4fd998082a9e753f25dee15/detectron2/modeling/roi_heads/fast_rcnn.py#L301" rel="noreferrer">code</a>.</p>
<p>For more details on the losses (3) and (4), take a look at the <a href="https://arxiv.org/abs/1506.01497" rel="noreferrer">Faster R-CNN paper</a> and the <a href="https://github.com/facebookresearch/detectron2/blob/5e2a1ecccd228227c5a605c0a98d58e1b2db3640/detectron2/modeling/proposal_generator/rpn_outputs.py#L302" rel="noreferrer">code</a>.</p>
<p>For more details on the loss (5), take a look at the <a href="https://arxiv.org/abs/1703.06870" rel="noreferrer">Mask R-CNN paper</a> and the <a href="https://github.com/facebookresearch/detectron2/blob/cc2d218a572c2bfea4fd998082a9e753f25dee15/detectron2/modeling/roi_heads/mask_head.py#L23" rel="noreferrer">code</a>.</p>
</li>
</ol>
<p><code>time</code>: Time taken by the iteration.</p>
<p><code>data_time</code>: Time taken by the dataloader in that iteration.</p>
<p><code>lr</code>: The learning rate in that iteration.</p>
<p><code>max_mem</code>: Maximum GPU memory occupied by tensors in bytes.</p>
What is total_loss,loss_cls etc
<p>I want to train a custom dataset on using faster_rcnn or mask_rcnn with the Pytorch and Detectron2 .Everything works well but I wanted to know I want to know what are the results I have.</p>
<pre><code>[11/29 20:16:31 d2.utils.events]:  eta: 0:24:04  iter: 19  total_loss: 9.6  loss_cls: 1.5  loss_box_reg: 0.001034  loss_mask: 0.6936  loss_rpn_cls: 6.773  loss_rpn_loc: 0.5983  time: 1.4664  data_time: 0.0702  lr: 4.9953e-06  max_mem: 2447M
</code></pre>
<p>I have this as result and I want to know what all of this means</p>