To visualize an ONNX model, we can use the net drawer tool. This tool takes in a serialized ONNX model and produces a directed graph representation. Let's walk through an example visualizing a SqueezeNet model exported from Pytorch. In the assets folder, you should find a file named squeezenet.onnx. This is a serialized SqueezeNet model that was exported to ONNX from PyTorch.
Keras is a deep learning library that wraps the efficient numerical libraries Theano and TensorFlow. In this post you will discover how to develop and evaluate neural network models using Keras for a regression problem. Regression Tutorial with Keras Deep Learning Library in Python Photo by Salim Fadhley, some rights reserved. The problem that we will look at in this tutorial is the Boston house price dataset. You can download this dataset and save it to your current working directly with the file name housing.csv.
One of the most amazing things about Python's scikit-learn library is that is has a 4-step modeling pattern that makes it easy to code a machine learning classifier. While this tutorial uses a classifier called Logistic Regression, the coding process in this tutorial applies to other classifiers in sklearn (Decision Tree, K-Nearest Neighbors etc). In this tutorial, we use Logistic Regression to predict digit labels based on images. The image above shows a bunch of training digits (observations) from the MNIST dataset whose category membership is known (labels 0–9). After training a model with logistic regression, it can be used to predict an image label (labels 0–9) given an image.
OpenVSLAM is a monocular, stereo, and RGBD visual SLAM system. One of the noteworthy features of OpenVSLAM is that the system can deal with images captured with multiple camera models, such as perspective, fisheye, and equirectangular. RICOH THETA series, insta360 series, etc) is presented above. Some code snippets to realize core functionalities of the system are provided for convenience of users. You can employ these snippets for their own programs.