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How to implement TIDE for custom dataset?
In my dataset i have one half as COCO dataset and other half as custom added dataset. So now, how should i check performance of model? Can you please explain in step by step?
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pycocotools' dataset abstraction does not provided an API that's ergonomic for non-COCO data, but this library does; it's why I'm trying it out. Make a Data instance for you ground truth data, and one for your test data, fill them up using the add_* methods, and then trigger evaluation. I just noticed that Tide does NOT rle compress masks as you add them, so beware of high RAM usage. Data.add_detection and friends to NOT rle compress masks as you add them... hopefully it won't complain if you compress them using calls to pycocotols before passing them in.
Any updates regarding adding your own custom dataset drivers?
If someone wants an example of what @drewm1980 had mentioned to get a custom dataset driver working, here's an example of another non-COCO-related dataset working (VOC2007): https://gist.github.com/vjsrinivas/56ca6e209adf23be17b9d2266b288c71
It's still calculating the 101-point interpolation in COCO AP so it is not equal to VOC2007 mAP eval, but you can probably adjust it to get the same output values.