simurgailab/mask-rcnn-implementation-with-custom-dataset
Implementation of Mask R-CNN architecture, one of the object recognition architectures, on a custom dataset.
This project helps machine learning engineers and researchers implement a Mask R-CNN model for object detection and instance segmentation on their unique datasets. It takes a custom dataset with JSON annotations defining objects and their pixel-level masks, and outputs a trained model capable of identifying objects and outlining their exact shapes in new images. This tool is for those who need to detect and segment specific objects not covered by standard, pre-trained models.
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Use this if you need to train a Mask R-CNN model from scratch or fine-tune it to precisely identify and segment objects in images from your own specialized dataset.
Not ideal if you are looking for a plug-and-play solution for common object detection tasks or if you don't have a labeled dataset for training.
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Last pushed
Nov 01, 2022
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