ReductStore is a high-throughput, time-series object store optimized for edge computing and AI/ML workflows, delivering tailored solutions for managing sequential data efficiently at scale.
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ReductStore is a time series database that is specifically designed for storing and managing large amounts of blob data. It boasts high performance for both writing and real-time querying, with the added benefit of batching data.
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Rust, tokio, axios
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ReductStore offers better performance and provides a retention policy based on disk usage and conditional append-only replication for your data reduction strategy.
ReductStore's answer
Edge computing, computer vision, and IoT engineers
ReductStore is an excellent choice for anyone looking for a powerful and reliable time series database for binary data. The user-friendly HTTP API makes it easy to work with, and the focus on edge computing ensures that data is always available when you need it. The ability to filter records using labels makes it easy to find the data you need quickly.
Based on our record, Computer Vision Annotation Tool (CVAT) should be more popular than ReductStore. It has been mentiond 14 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
ReductStore is a specialized time-series database designed for blob data, optimized for edge computing, computer vision, and IoT applications. - Source: dev.to / 4 months ago
When seeking an on-premise alternative to LandingEdge for sophisticated edge computing and computer vision tasks, ReductStore emerges as a compelling solution. This Time-Series Database is tailored for Blob Data, emphasizing optimizations that cater to the unique demands of Edge Computing, IoT, and Computer Vision applications. - Source: dev.to / 6 months ago
Another powerful resource is CVAT, the Computer Vision Annotation Tool which supports both image and video annotations with advanced capabilities such as interpolation of shapes between frames, making it highly suitable for computer vision. - Source: dev.to / 6 months ago
CVAT has an open source repo under MIT license: https://github.com/opencv/cvat I've not worked with it directly but it might be a good place to start. Source: 6 months ago
An open source annotation tool that integrates object detectors is CVAT https://github.com/opencv/cvat however, using your own detector might require some coding. There is an integration for yolov5, but without modification it only loads the pretrained models. Source: about 1 year ago
This integration is currently available in the open-source version of Computer Vision Annotation Tool (http://github.com/opencv/cvat)! Please use it for your computer vision projects to segment images faster. - Source: Hacker News / about 1 year ago
You can download the CVAT docker from a github (Link) and install it yourself, keeping all data local. And here are two options - locally on your personal computer (or company server) or in your own cloud (there are instructions on how to do this with AWS). - Source: dev.to / about 1 year ago
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