Detector Training Examples¶
This document corresponds to this example online, in addition to the object_detector_training example folder in a VIAME installation.
The common detector training API is used for training multiple object detectors from the same input format for both experimentation and deployment purposes. By default, each detector has a default training process that handles issues such as automatically reconfiguring networks for different output category labels, while simulatenously allowing for more customization by advanced users.
Future releases will also include the ability to use stereo depth maps in training, alongside additional forms of data augmentation and more easily definable data source nodes for alternative input file structures.
where groundtruth can be in any file format for which a “detected_object_set_input” implementation exists (e.g. viame_csv, kw18, habcam), and labels.txt contains a list of output categories (one per line) for the trained detection model. “labels.txt” can also contain any alternative names in the groundtruth which map back to the same output category label. For example, see training_data/labels.txt for the corresponding groundtruth file in training_data/seq1. The “labels.txt” file allows the user to selectively train models for certain sub-categories or super-categories of object by specifying only the categories of interest to train a model for, and any synonyms for the same category on the same line.
After formatting data, a model can be trained via the ‘viame_train_detector’ tool, the only modification required from the scripts in this folder being setting your .conf files to the correct groundtruth file format type.
These are the build flags required to run this example, if building from the source.
In the pre-built binaries they are all enabled by default.