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EVA Provides a secure and consistent platform : Development Pipeline Components
The EVS development pipeline uses several tools to accelerate the process from data annotation to model deployment:
CVAT (Computer Vision Annotation Tool): An open-source, web-based tool for annotating images and videos for computer vision tasks. It's designed for collaborative teams and supports various annotation formats like bounding boxes, polygons, and key-points.
Label Studio: A versatile open-source data annotation tool that can handle a wide range of data types, including images, video, text, and audio. It's known for its flexibility and ease of integration into existing machine learning workflows.
VGG Image Annotator (VIA): A lightweight, simple tool for image annotation that runs directly in a web browser. While less feature-rich than CVAT or Label Studio, it is excellent for quick, one-off projects or for users who don't need a complex setup.
ONNX Compatibility
A critical feature of the EVS pipeline is its ability to produce ONNX-compatible results. ONNX, which stands for Open Neural Network Exchange, is an open format designed to represent machine learning models.
This compatibility is significant because it allows a model trained in one framework (like TensorFlow or PyTorch) to be exported and run in another environment or framework that supports ONNX. This creates a flexible, hardware-agnostic pipeline, which is a major advantage for EVS and its use of IBM Power hardware.
Benefits for Time and Money Savings
The integration of these tools and the ONNX compatibility offers several benefits:
Faster Iteration: The tools automate and streamline the often time-consuming process of data labeling, which is a key bottleneck in computer vision projects. This enables development teams to quickly move from data annotation to model training and deployment.
Reduced Costs: By using GPU-free inferencing on the IBM Power platform, EVS can significantly lower both the initial hardware costs and ongoing energy consumption associated with traditional AI workloads that rely heavily on GPUs.
Interoperability: ONNX compatibility ensures that the models developed can be easily deployed and optimized for the IBM Power hardware, without being locked into a specific framework. This provides flexibility and future-proofs the solution.
Scalability: The combined solution of EVS, IBM Power, and these open-source tools creates a robust, scalable platform for deploying computer vision at the edge and in data centers.
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