akankshakshurai/cuda-error-resolution-analysis huggingface dataset:
PyTorch Detection Models COCO AP

username: Laya1
cooked: <p>I loaded the PyTorch Detection models as:<br>
<code>model_frcnn = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True) model_retinanet = torchvision.models.detection.retinanet_resnet50_fpn(pretrained=True)</code></p>
<p>This should load the models that are pretrained on COCO dataset. I do not do any changes to the models and just evaluate the models on the COCO val2017 dataset using Pycocotools. I get the following results on Faster RCNN and Retinanet:<br>
<div class="lightbox-wrapper"><a class="lightbox" href="https://discuss.pytorch.org/uploads/default/original/3X/d/3/d37cf00bc4d6d245d12709afd43bf8204b2b812c.png" data-download-href="https://discuss.pytorch.org/uploads/default/d37cf00bc4d6d245d12709afd43bf8204b2b812c" title="frrcnn"><img src="https://discuss.pytorch.org/uploads/default/original/3X/d/3/d37cf00bc4d6d245d12709afd43bf8204b2b812c.png" alt="frrcnn" data-base62-sha1="uaUqhT9uwPKU7Pf8EUgDFG5h7fC" width="690" height="232" data-dominant-color="384049"><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">frrcnn</span><span class="informations">1029×346 8.65 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/1/7/17b91a8fb94b523b5867b59bee5d318c9393e752.png" data-download-href="https://discuss.pytorch.org/uploads/default/17b91a8fb94b523b5867b59bee5d318c9393e752" title="retinanet"><img src="https://discuss.pytorch.org/uploads/default/original/3X/1/7/17b91a8fb94b523b5867b59bee5d318c9393e752.png" alt="retinanet" data-base62-sha1="3nRz12GZdvGPEEJCfjAVMr1JKjU" width="690" height="232" data-dominant-color="3F4750"><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">retinanet</span><span class="informations">1029×346 8.23 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>It looks like the AP is 9%. Is this the expected output? How is the AP so low for a pretrained model?</p>
CenterNet Network Output

username: alienshallowalchemy
cooked: <p>Hello</p>
<p>I am new to object detection and I was reading the centernet paper titled “CenterNet Keypoint Triplets for Object Detection” and I was left with some doubts.</p>
<ol>
<li>
<p>I am familiar with CNN’s am the general principle of image convolution-bn-maxpool until the features flattening until sigmoid/softmax classification. However, from what I understand object detection adapts an autoencoder backbone right?</p>
</li>
<li>
<p>the so-called keypoints are an output of the encoder-decoder backbone? or the output is just an image reconstructed from the decoder?</p>
</li>
<li>
<p>is there any coordinates as output of the decoder?</p>
</li>
<li>
<p>The part of heatmaps, center pooling and offsets are purely an image segmentation process?</p>
</li>
<li>
<p>loss function is always pixel-wise? the ground truth is the original image? or is it the label of the original image?</p>
</li>
</ol>
<p>Thank you in advance for your time and help</p>
Variational Gaussian Process Dynamical Systems

username: AndreaBraschi
cooked: <p>Hi all,</p>
<p>I’ve been trying to implement a variational GP dynamical system using GPyTorch.</p>
<p>I’m using the class VariationalLatentVariable to recover a variational approximation of the latent variable (approximation of q(x) ). For this reason, I need to define a prior over X. In my case, I am trying to define a Multivariate Normal Prior with mean = 0 and covariance matrix constructed using the RBF kernel which takes the time-data as inputs.</p>
<p>Unfortunately, as I try to define such prior distribution I get the following error:</p>
<p>“RuntimeError: Invalid expand arguments (98, -1, -1). Currently, repeat only works to create repeated batches of a 2D LazyTensor”</p>
<p>where 98 is the length of the data.</p>
<p>I’ve tried to convert the LazyTensor that is defined as covariance matrix into a torch.Tensor and use that to define the Multivariate Normal Prior, but the matrix appears not to be a positive semidefinite.</p>
<p>I’ve also tried to define the prior over X using a Multivariate Normal from gpytorch.distributions.MultivariateNormal, and the distribution is defined without problems, but in this case the issue comes later with the VariationalLatentVariable class that needs a gpytorch.priors.Prior as an input for the prior over X.</p>
<p>Anyone familiar with the GPyTorch library that has an idea on how I can get around this issue?</p>
<p>Any help will be much appreciated.</p>
<p>Cheers,</p>
<p>Andrea</p>