IBM PowerAI Vision Version 1.1.3
About PowerAI Vision
PowerAI Vision can help provide robust end-to-end workflow support for deep learning models related to computer vision. This enterprise-grade software provides a complete ecosystem to label raw data sets for training, creating, and deploying deep learning-based models. PowerAI Vision is designed to empower subject matter experts with no skills in deep learning technologies to train models for AI applications. It can help train highly accurate models to classify images and detect objects in images and videos.
PowerAI Vision is built on open source frameworks for modeling and managing containers to deliver a highly available framework, providing application lifecycle support, centralized management and monitoring, and support from IBM. Learn more:
What’s new in PowerAI Vision Version 1.1.3
PowerAI V1.1.3 builds upon previous releases and includes the following updates and features:
- GPU sharing for deployed models
The full version of PowerAI Vision now supports GPU sharing for deployed models. Deploying multiple models to a single GPU allows you to get the most out of your processing power. GPU sharing is supported only for GoogleNet and Faster R-CNN models. For more information, see Deploying a trained model.
- Train with a Detectron model
You can now use a Detectron model to train a model. This allows you to train with objects that have been labeled as non-rectangular shapes. For details, see Training a model.
- Transfer learning
You can use a model that was previously trained with PowerAI Vision as a base model to train new models. For details, see Training a model.
- Use non-rectangular shapes when labeling
When labeling objects in a data set that will be used to train a Detectron model, you can use non-rectangular shapes. Non-rectangular labeling is supported in images, video frames, and with auto labeling. If you label objects with non-rectangular shapes and train the data set using a different model, associated rectangular bounding boxes are used. For more information, see Labeling objects.
- Support of COCO annotations
Images with COCO annotations can be imported. Only object detection annotations are supported. For more information, see Importing images with COCO annotations.
- Downloadable heat map
You can download the heat map that is generated when testing an image with a deployed model.
- Decrypting a trained model
You can decrypt a model in PowerAI Vision Inference Server. See PowerAI Vision Inference Server for details.
- Improved performance for inference
Speeds when using the image classification (GoogLeNet) and object detection (Faster R-CNN) models for inference are improved. The improvement is especially significant for high-resolution images.
- Improvements to the user interface
- IBM Power System AC922 with NVIDIA Tesla V100 GPUs
- IBM Power System S822LC with NVIDIA Tesla P100 GPUs
Supported operating systems
- Red Hat Enterprise Linux 7.6
- Ubuntu 18.04 is fully supported
- Install information
PowerAI Vision code patterns
Check out these real world examples and tutorials that highlight PowerAI Vision in action.
- Model for counting cars
- Identify various flavors of coke
- Integrate inference on mobile phones
- Build and deploy a PowerAI Vision model and use it in an iOS app
Tell us what you think
Requesting enhancements for IBM PowerAI Vision
The IBM Request for Enhancement (RFE) tool is now available for you to submit formal enhancement requests to the PowerAI Vision development team. One of the benefits of using the RFE tool is that other clients can vote on submitted requirements, which helps IBM to prioritize requests.
Go here get started: ibm.biz/vision-rfe
The RFE for PowerAI Vision pages are part of IBM Developer and require that you sign in with an IBM ID to submit or vote on a request. You should make sure that your IBM ID profile includes your current company and your email address to ensure that we can contact you if we have questions.
Once on the RFE page, click on the âSearchâ tab to view existing requests before you submit a new request. It is much more useful to vote for a previously submitted request than to submit a duplicate request.