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You are part of a project that will use deep learning to try to identify what is in images - such as cars, ducks, mountains, sky, trees, etc. In this project, two things are important - the first one, is that the deep learning model trains quickly, with efficiency (because...
Cássia Sampaio
Neural Radiance Fields, colloquially known as NeRFs have struck the world by storm in 2020, released alongside the paper "NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis", and are still the cornerstone of high quality synthesis of novel views, given sparse images and camera positions. Since...
David Landup
Suppose you want your Keras model to have some specific behavior during training, evaluation or prediction. For instance, you might want to save your model at every training epoch. One way of doing this is using Callbacks. In general, Callbacks are functions that are called when some event happens, and...
Felipe Antunes
Object detection has been gaining steam, and improvements are being made to several approaches to solving it. In the past couple of years, YOLO-based methods have been outperforming others in terms of accuracy and speed, with recent advancements such as YOLOv7 and YOLOv6 (which was released independently, after YOLOv7). However...
Object detection is a large field in computer vision, and one of the more important applications of computer vision "in the wild". From it, keypoint detection (oftentimes used for pose estimation) was extracted. Keypoints can be various points - parts of a face, limbs of a body, etc....
Object detection is a large field in computer vision, and one of the more important applications of computer vision "in the wild". From it, instance segmentation was extracted, and is tasked with having models predict not only the label and bounding box of an object, but also the...
The learning rate is an important hyperparameter in deep learning networks - and it directly dictates the degree to which updates to weights are performed, which are estimated to minimize some given loss function. In SGD: $$ weight_{t+1} = weight_t - lr * \frac{derror}{dweight_t} $$ With a learning...
Improving the performance of a training loop can save hours of computing time when training machine learning models. One of the ways of improving the performance of TensorFlow code is using the tf.function() decorator - a simple, one-line change that can make your functions run significantly faster. In this...
Data augmentation has, for a long while, been serving as a means of replacing a "static" dataset with transformed variants, bolstering the invariance of Convolutional Neural Networks (CNNs), and usually leading to robustness to input. Note: Invariance boils down to making models blind to certain pertubations, when making...
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