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Training dataset formed by two types of elements (for now) to be detected by the model using Deep Learning: Boiler and Radiator. Of the boiler type 107 images have been collected and of the radiator type 123 images. Although there are few images to get good training, it can be used as a first approximation and to evaluate the precision obtained with few training images. These images have been distributed in 80% of the images for the training phase and 20% for the test phase. All the images have the bounding box data for each element in the xml file with the same name. Additionally, 33 other images (not previously used) have been used for the validation of the tool.
README.txt
Text — 509 BTraining dataset for HVAC detection.zip
ZIP Archive — 66.95 MBA zip archive containing images of HVAC systems together with metadata (inclusive labels) in xml and json format. The xml files are in Pascal VOC XML format which is widely used in Deep Learning. Json files are used in COCO, also widely used. There are conversion tools from one format to another. For more information: https://towardsai.net/p/machine-learning/understanding-pascal-voc-and-coco-annotations-for-object-detection https://towardsdatascience.com/coco-data-format-for-object-detection-a4c5eaf518c5