Where are pre-trained ML models?

Pre-trained machine learning models available in AWS Marketplace.

Why we use pre-trained model?

Simply put, a pre-trained model is a model created by some one else to solve a similar problem. Instead of building a model from scratch to solve a similar problem, you use the model trained on other problem as a starting point. For example, if you want to build a self learning car.

What makes a good training dataset?

What factors are to be Considered when Building a Machine Learning Training Dataset? You need to assess and have an answer ready for these basic questions around the quantity of data: The number of records to take from the databases. The size of the sample needed to yield expected performance outcomes.

What is optical flow and why does it matter in deep learning?

Optical flow is a powerful idea and it has been used to significantly improve accuracy when classifying videos and at a lower computational costs. It has been around since the 1980s existing in the form of hand crafted approaches. Thus the optical flow displacement vector for this motion will be [9, 5 ]. …

Where can I find TensorFlow models?

Explore repositories and other resources to find available models, modules and datasets created by the TensorFlow community.

1. TensorFlow Hub. A comprehensive repository of trained models ready for fine-tuning and deployable anywhere.
2. Model Garden.
3. TensorFlow.js models.

What is pre-trained data?

Pre-training in AI refers to training a model with one task to help it form parameters that can be used in other tasks. The concept of pre-training is inspired by human beings. That is: using model parameters of tasks that have been learned before to initialize the model parameters of new tasks.

How do you create a training dataset?

Steps for Preparing Good Training Datasets

1. Identify Your Goal. The initial step is to pinpoint the set of objectives that you want to achieve through a machine learning application.
2. Select Suitable Algorithms. different algorithms are suitable for training artificial neural networks.
3. Develop Your Dataset.

How do you get data for machine learning?

Popular sources for Machine Learning datasets

1. Kaggle Datasets.
2. UCI Machine Learning Repository.
3. Datasets via AWS.
4. Google’s Dataset Search Engine.
5. Microsoft Datasets.
6. Awesome Public Dataset Collection.
7. Government Datasets.
8. Computer Vision Datasets.

Why do we need optical flow?

Optical flow was used by robotics researchers in many areas such as: object detection and tracking, image dominant plane extraction, movement detection, robot navigation and visual odometry. Optical flow information has been recognized as being useful for controlling micro air vehicles.

What is optical flow in machine learning?

Optical Flow Estimation is the problem of finding pixel-wise motions between consecutive images. Approaches for optical flow estimation include correlation-based, block-matching, feature tracking, energy-based, and more recently gradient-based. Further readings: Optical Flow Estimation.

What is open model zoo?

The Open Model Zoo includes the following demos: 3D Human Pose Estimation Python* Demo – 3D human pose estimation demo. 3D Segmentation Python* Demo – Segmentation demo segments 3D images using 3D convolutional networks.