Good Reasons to Use Pycocotools

Among the many libraries available for Python, OpenCV, and Lua, pycocotools stands out as a worthy contender for best in class. It may be best known for its python support, but it also boasts a well-designed interface that makes it easy to use for non-Python users. It also comes with a useful set of tutorials to get you up and running in no time.

One of the many perks of pycocotools is the ability to perform image manipulation with a few clicks of the mouse. In addition, it provides a python based API to assist with the loading and parsing of COCO annotations. For a hands-on approach, you can also opt for the pycoco-test-enabled command line option. It also comes with a slew of prebuilt functions that make it easy to create, modify, and manipulate COCO data in your favorite language.

Another good reason to use pycocotools is the fact that it can also be used as a proof of concept tool, akin to a good pair of glasses. It also allows you to visualize your bbox datasets in a standardized format, which is great for comparing the results of your latest iteration with the results of the ye olde standby.

Finally, pycocotools comes with a free library of COCO benchmarks, allowing you to test the performance of your own algorithms with a minimum of fuss. The COCO benchmarks are also available in a variety of formats, including python, OpenCV, and pycoco-test-enabled. In fact, the COCO benchmarks can also be downloaded as an iframe in the OpenCV API librarry. You can also download and test the pycoco-test-enabled benchmarks for free by signing up for pycocotools’s free trial. It is a powerful tool that is well worth the time and effort it takes to learn it. Among other things, it comes with the pycocotools package, a collection of python based APIs for image manipulation and the parsing of COCO annotations. It also comes with a suite of tutorials to get you up and running on the most common COCO command line commands.