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#convolutionalneuralnetworks

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feitgemel<p>How to classify Malaria Cells using Convolutional neural network</p><p>You can find link for the code in the blog : <a href="https://eranfeit.net/how-to-classify-malaria-cells-using-convolutional-neural-network/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">eranfeit.net/how-to-classify-m</span><span class="invisible">alaria-cells-using-convolutional-neural-network/</span></a></p><p>Check out our tutorial here : <a href="https://youtu.be/WlPuW3GGpQo&amp;list=UULFTiWJJhaH6BviSWKLJUM9sg" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">youtu.be/WlPuW3GGpQo&amp;list=UULF</span><span class="invisible">TiWJJhaH6BviSWKLJUM9sg</span></a></p><p>Enjoy<br>Eran</p><p><a href="https://mastodon.social/tags/Python" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Python</span></a> <a href="https://mastodon.social/tags/imageclassification" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>imageclassification</span></a> <a href="https://mastodon.social/tags/convolutionalneuralnetworks" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>convolutionalneuralnetworks</span></a> <a href="https://mastodon.social/tags/transferlearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>transferlearning</span></a></p>
Laurent Perrinet<p><a href="https://neuromatch.social/tags/ConvolutionalNeuralNetworks" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ConvolutionalNeuralNetworks</span></a> (<a href="https://neuromatch.social/tags/CNNs" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>CNNs</span></a> in short) are immensely useful for many <a href="https://neuromatch.social/tags/imageProcessing" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>imageProcessing</span></a> tasks and much more... </p><p>Yet you sometimes encounter some bits of code with little explanation. Have you ever wondered about the origins of the values for image normalization in <a href="https://neuromatch.social/tags/imagenet" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>imagenet</span></a> ?</p><ul><li>Mean: <code>[0.485, 0.456, 0.406]</code> (for R, G and B channels respectively)</li><li>Std: <code>[0.229, 0.224, 0.225]</code></li></ul><p>Strangest to me is the need for a three-digits precision. Here, after finding the origin of these numbers for MNIST and ImageNet, I am testing if that precision is really important : guess what, it is not (so much) !</p><p>👉 if interested in more details, check-out <a href="https://laurentperrinet.github.io/sciblog/posts/2024-12-09-normalizing-images-in-convolutional-neural-networks.html" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">laurentperrinet.github.io/scib</span><span class="invisible">log/posts/2024-12-09-normalizing-images-in-convolutional-neural-networks.html</span></a></p>
feitgemel<p>📽️ In our latest video tutorial, we will create a dog breed recognition model using the NasLarge pre-trained model 🚀 and a massive dataset featuring over 10,000 images of 120 unique dog breeds 📸.</p><p>Check out our tutorial here : <a href="https://youtu.be/vH1UVKwIhLo&amp;list=UULFTiWJJhaH6BviSWKLJUM9sg" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">youtu.be/vH1UVKwIhLo&amp;list=UULF</span><span class="invisible">TiWJJhaH6BviSWKLJUM9sg</span></a></p><p>You can find link for the code in the blog : <a href="https://eranfeit.net/120-dog-breeds-more-than-10000-images-deep-learning-tutorial-for-dogs-classification/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">eranfeit.net/120-dog-breeds-mo</span><span class="invisible">re-than-10000-images-deep-learning-tutorial-for-dogs-classification/</span></a></p><p>Enjoy<br>Eran</p><p><a href="https://mastodon.social/tags/Python" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Python</span></a> <a href="https://mastodon.social/tags/Cnn" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Cnn</span></a> <a href="https://mastodon.social/tags/TensorFlow" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>TensorFlow</span></a> <a href="https://mastodon.social/tags/deeplearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>deeplearning</span></a> <a href="https://mastodon.social/tags/neuralnetworks" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>neuralnetworks</span></a> <a href="https://mastodon.social/tags/imageclassification" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>imageclassification</span></a> <a href="https://mastodon.social/tags/convolutionalneuralnetworks" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>convolutionalneuralnetworks</span></a> <a href="https://mastodon.social/tags/computervision" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>computervision</span></a> <a href="https://mastodon.social/tags/transferlearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>transferlearning</span></a></p>
IT News<p>VOCHI raises additional $2.4 million for its computer vision-powered video editing app - VOCHI, a Belarus-based startup behind a clever computer vision-based video editing... - <a href="http://feedproxy.google.com/~r/Techcrunch/~3/ye8jKOj7GIM/" rel="nofollow noopener" target="_blank"><span class="invisible">http://</span><span class="ellipsis">feedproxy.google.com/~r/Techcr</span><span class="invisible">unch/~3/ye8jKOj7GIM/</span></a> <a href="https://schleuss.online/tags/convolutionalneuralnetworks" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>convolutionalneuralnetworks</span></a> <a href="https://schleuss.online/tags/artificialintelligence" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>artificialintelligence</span></a> <a href="https://schleuss.online/tags/image" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>image</span></a>-processing <a href="https://schleuss.online/tags/fundings" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>fundings</span></a>&amp;exits <a href="https://schleuss.online/tags/computervision" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>computervision</span></a> <a href="https://schleuss.online/tags/onlinecreators" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>onlinecreators</span></a> <a href="https://schleuss.online/tags/recentfunding" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>recentfunding</span></a> <a href="https://schleuss.online/tags/videoediting" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>videoediting</span></a> <a href="https://schleuss.online/tags/socialmedia" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>socialmedia</span></a> <a href="https://schleuss.online/tags/startups" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>startups</span></a> <a href="https://schleuss.online/tags/creators" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>creators</span></a> <a href="https://schleuss.online/tags/funding" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>funding</span></a> <a href="https://schleuss.online/tags/belarus" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>belarus</span></a> <a href="https://schleuss.online/tags/mobile" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>mobile</span></a> <a href="https://schleuss.online/tags/social" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>social</span></a> <a href="https://schleuss.online/tags/videos" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>videos</span></a> <a href="https://schleuss.online/tags/media" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>media</span></a> <a href="https://schleuss.online/tags/video" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>video</span></a> <a href="https://schleuss.online/tags/apps" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>apps</span></a></p>
heise online (inoffiziell)Das Sortieren von verzerrten Scherben gehört zur grundlegenden Arbeit in der Archäologie, gerne macht es aber wohl niemand. Algorithmen könnten es übernehmen. <a href="https://www.heise.de/news/Archaeologie-KI-klassifiziert-Scherben-besser-und-nachvollziehbarer-als-Menschen-6054446.html" rel="nofollow noopener" target="_blank">Archäologie: KI klassifiziert Scherben besser und nachvollziehbarer als Menschen</a>
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