machine learning

3D Packing for Self-Supervised Monocular Depth Estimation -------------------------------------------------------------- by Vitor Guizilini, `pdf at arxiv `_, 2020 Learning 1. Depth estimator :math:`f_D : I \rightarrow D` 2. Ego motion estimator: :math:`f_x : (I_t , I_S) \rightarrow x_{t \rightarrow S}` Depth Estimator ===================================== They predict an inverse depth and use a packnet architecture. Inverse depth probably has more stable results. Points far away from camera have small inverse depth that with low precision....

In 2020 which architecture should I use for my image classification/tracking/segmentation/… task? I was asked on an interview that and I didn’t have a prepared answer. I made a small research and want to write down some thoughts. Most of the architectures build upon ideas from ResNet paper Deep Residual Learning for Image Recognition, 2015 Here is some explanation of resnet family:An Overview of ResNet and its Variants by Vincent Fung, 2017....

fast.ai library has a pretty easy to use yet powerful capabilities for semantic image segmentation. By default all the classes are treated the same. The network is trained to predict all the labels. Sometimes it’s important to provide non-complete labeling. That means for some areas the label is undefined. The performance of the network should exclude that areas in the loss and accuracy computation. That allows the network predict any other class in those areas....

TensorFlow Serving, sources - library for serving machine learning models. Written in C++ and Python. Server is in C++. Requires Bazel - Google’s build tool. Doesn’t work with python 3. Probably fast. TensorFlow: How to freeze a model and serve it with a python API Building a Machine Learning App with AWS Lambda (slides) Pipeline.io - End-to-End, Continuous Spark ML + Tensorflow AI Data Pipelines, Sources Interesting thread. They propose to use “saved_model_cli binary (in tools/), which you can feed a SavedModel, and pass input data via files....

Super harsh guide to machine Learning Super harsh guide to machine learning (reddit) First, read fucking Hastie, Tibshirani, and whoever. Chapters 1-4 and 7. If you don’t understand it, keep reading it until you do. You can read the rest of the book if you want. You probably should, but I’ll assume you know all of it. Take Andrew Ng’s Coursera. Do all the exercises in Matlab and python and R....

Implemented using python https://github.com/serge-m/object_detection_ir_video