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