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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Video and Image Analytics for Multiple Environments
| Date | Stars |
|---|---|
| 2026-07-24 | 336 |
| 2026-07-25 | 336 |
| 2026-07-28 | 336 |
| 2026-07-30 | 336 |
| 2026-08-06 | 336 |
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<img src="http://www.viametoolkit.org/wp-content/uploads/2016/08/viami_logo.png" alt="VIAME Logo" width="200" height="78"> VIAME is a computer vision application designed for do-it-yourself artificial intelligence including object detection, object tracking, image/video annotation, query-based search, image mosaicing, image enhancement, size measurement, multi-camera data processing, rapid model generation, and tools for the evaluation of different algorithms. Originally targeting marine species analytics, VIAME now contains many common algorithms and libraries, and is also useful as a generic computer vision toolkit. It contains a number of standalone tools for accomplishing the above, a pipeline framework which can connect C/C++, python, and matlab nodes together in a multi-threaded fashion, and multiple algorithms resting on top of the pipeline infrastructure. Lastly, a portion of the algorithms have been integrated into both desktop and web user interfaces for deployments in different types of environments, with an open annotation archive and example of the web platform available at [viame.kitware.com](https://viame.kitware.com). Documentation ------------- The [User's Quick-Start Guide](https://viame.readthedocs.io/en/latest/sections/quick_start_guide.html) and [Full Manual](http://viame.readthedocs.io/en/latest/) are more comprehensive, but select entries are also listed below broken down by individual functionality: [Documentation Overview](https://viame.readthedocs.io/en/latest/#documentation-overview) <> [Installation](examples/installing_from_binaries) <> [Building](examples/building_from_source) <> [All Examples](https://github.com/Kitware/VIAME/tree/master/examples) <> [DIVE Interface](https://kitware.github.io/dive) <> [VIEW Interface](examples/annotation_and_visualization) <> [Search and Rapid Model Generation](examples/search_and_rapid_model_generation) <> [Object Detector CLI](examples/object_detection) <> [Object Tracker CLI](examples/object_tracking) <> [Detector Training CLI](examples/object_detector_training) <> [Evaluation of Detectors](examples/scoring_and_evaluation) <> [Detection File Formats](https://viame.readthedocs.io/en/latest/sections/detection_file_conversions.html) <> [Calibration and Image Enhancement](examples/image_enhancement) <> [Registration and Mosaicing](examples/registration_and_mosaicing) <> [Stereo Measurement and Depth Maps](examples/size_measurement) <> [Pipelining Overview](https://github.com/Kitware/kwiver) <> [Core Class and Pipeline Info](https://kwiver.readthedocs.io/en/latest) <> [Plugin Integration](examples/example_pipeline) <> [Example Plugin Templates](plugins/templates) <> [Embedding Algorithms in C++](examples/using_algorithms_in_code) Installations ------------- For a full installation guide and description of the various flavors of VIAME, see the quick-start guide, above. The full desktop version is provided as either a .msi, .zip or .tar file. Alternatively, standalone annotators (without any processing algorithms) are available via smaller installers (see DIVE standalone, below). Lastly, docker files are available for both VIAME Desktop and Web (below). For full desktop installs, extract the binaries and place them in a directory of your choosing, for example /opt/noaa/viame on Linux or C:\Program Files\VIAME on Windows. If using packages built with GPU support, make sure to have sufficient video drivers installed, version 570.65 or higher. The best way to install drivers depends on your operating system. This isn't required if just using manual annotators (or frame classifiers only). The binaries are quite large, in terms of disk space, due to the inclusion of multiple default model files and programs, but if just building your desired features from source (e.g. for embedded apps) they are much smaller. **Installation Requirements:** <br> * Up to 8 Gb of Disk Space for the Full Installation <br> * Windows 7\*, 8, 10, or 11 (64-Bit) or Linux (64-Bit, e.g.
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:8671f7b3864cab63, topic:computer-vision, topic:object-detection, readme:computer vision