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53 lines
2.1 KiB
53 lines
2.1 KiB
# Single class pedestrian detector for SOD RealNets.
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#
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# This file can serve as template for your future RealNets models to be generated by the SOD training
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# interfaces which are documented at https://sod.pixlab.io/api.html#realnet_train.
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#
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# Copyright (C) PixLab| Symisc Systems - All right reserved. legal@symisc.net - https://sod.pixlab.io
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# The first thing to specify is where the training samples are located.
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# You must group your dataset on the same directory so can SOD load each entry
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# on a single run and pass the collected image set to the RealNet trainer.
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[paths]
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# Mandatory positive samples path (i.e. the pedestrian dataset that may contains hundred or thousand of images)
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pos = /var/pedestrian_dataset/positives
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# Background samples path (i.e. various negative samples holding anything [car, trees, bus, cat, etc] except a pedestrian!! very important)
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neg = /var/pedestrian_dataset/background
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# Optional test sample path
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#test = /var/pedestrian_dataset/test
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# True to recurse (scan) subdirectories on the root path of your dataset (positives, background and test paths)
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recurse = true
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# Everything below is an optional field and does not require that you mess with it unless
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# you know what you doing (i.e. Tune your model)
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[detector]
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# min_tree_depth = 6 # Minimum tree depth
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# max_tree_depth = 12 # Maximum tree depth
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# max_trees = 2048 # Maximum decision tress to generate for this model
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# tpr = 0.9975 # Minimum True Positive Rate (TPR) which must be a float value set between 0.1 .. 1
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# fpr = 0.5 # Maximum False Positive Rate (FPR) which must be a float value set between 0.1 .. 1
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# data_augment = false # Introduce small perturbation to the input positive samples
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# target_fpr = 1e-6 # Target false positive rate (FPR) to achieve.
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# When we hit this value or max_trees whichever occurs first, training is stopped.
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# normalize = false # Normalize the training positive samples
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# Information about your model
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name = pedestrian
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about = RealNets pedestrian detector (single class) - Copyright (C) 2017 - 2018 Symisc Systems
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