From ee176000ebbde43a45aa8375a7052f61f73b7b15 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Sun, 18 Aug 2019 02:08:47 +0200 Subject: [PATCH] updates --- train.py | 70 ++++++++++++++++++++++++++++---------------------------- 1 file changed, 35 insertions(+), 35 deletions(-) diff --git a/train.py b/train.py index cea491c3..766b1112 100644 --- a/train.py +++ b/train.py @@ -33,45 +33,45 @@ except: # 0.268 0.268 0.178 0.240 4.36 1.104 5.596 0.2087 14.47 2.599 16.27 2.406 0.4114 0.001585 -4 0.950 0.000524 # hc # 0.161 0.327 0.190 0.193 7.82 1.153 4.062 0.1845 24.28 3.05 20.93 2.842 0.2759 0.001357 -4 0.916 0.000572 # hd 0.438 mAP @ epoch 100 -# # Hyperparameters (j-series, 50.5 mAP yolov3-320) evolved by @ktian08 https://github.com/ultralytics/yolov3/issues/310 -# hyp = {'giou': 1.582, # giou loss gain -# 'xy': 4.688, # xy loss gain -# 'wh': 0.1857, # wh loss gain -# 'cls': 27.76, # cls loss gain (CE=~1.0, uCE=~20, uBCE=~200,~30) -# 'cls_pw': 1.446, # cls BCELoss positive_weight -# 'obj': 21.35, # obj loss gain -# 'obj_pw': 3.941, # obj BCELoss positive_weight -# 'iou_t': 0.2635, # iou training threshold -# 'lr0': 0.010324, # initial learning rate -# 'lrf': -4., # final LambdaLR learning rate = lr0 * (10 ** lrf) -# 'momentum': 0.97, # SGD momentum -# 'weight_decay': 0.0004569, # optimizer weight decay -# 'hsv_s': 0.5703, # image HSV-Saturation augmentation (fraction) -# 'hsv_v': 0.3174, # image HSV-Value augmentation (fraction) -# 'degrees': 1.113, # image rotation (+/- deg) -# 'translate': 0.06797, # image translation (+/- fraction) -# 'scale': 0.1059, # image scale (+/- gain) -# 'shear': 0.5768} # image shear (+/- deg) - -# Hyperparameters (i-series) -hyp = {'giou': 1.43, # giou loss gain +# Hyperparameters (j-series, 50.5 mAP yolov3-320) evolved by @ktian08 https://github.com/ultralytics/yolov3/issues/310 +hyp = {'giou': 1.582, # giou loss gain 'xy': 4.688, # xy loss gain 'wh': 0.1857, # wh loss gain - 'cls': 11.7, # cls loss gain - 'cls_pw': 4.81, # cls BCELoss positive_weight - 'obj': 11.5, # obj loss gain - 'obj_pw': 1.56, # obj BCELoss positive_weight - 'iou_t': 0.281, # iou training threshold - 'lr0': 0.01, # initial learning rate + 'cls': 27.76, # cls loss gain (CE=~1.0, uCE=~20, uBCE=~200,~30) + 'cls_pw': 1.446, # cls BCELoss positive_weight + 'obj': 21.35, # obj loss gain + 'obj_pw': 3.941, # obj BCELoss positive_weight + 'iou_t': 0.2635, # iou training threshold + 'lr0': 0.010324, # initial learning rate 'lrf': -4., # final LambdaLR learning rate = lr0 * (10 ** lrf) 'momentum': 0.97, # SGD momentum - 'weight_decay': 0.000427, # optimizer weight decay - 'hsv_s': 0.0599, # image HSV-Saturation augmentation (fraction) - 'hsv_v': 0.142, # image HSV-Value augmentation (fraction) - 'degrees': 1.03, # image rotation (+/- deg) - 'translate': 0.0552, # image translation (+/- fraction) - 'scale': 0.0555, # image scale (+/- gain) - 'shear': 0.434} # image shear (+/- deg) + 'weight_decay': 0.0004569, # optimizer weight decay + 'hsv_s': 0.5703, # image HSV-Saturation augmentation (fraction) + 'hsv_v': 0.3174, # image HSV-Value augmentation (fraction) + 'degrees': 1.113, # image rotation (+/- deg) + 'translate': 0.06797, # image translation (+/- fraction) + 'scale': 0.1059, # image scale (+/- gain) + 'shear': 0.5768} # image shear (+/- deg) + +# # Hyperparameters (i-series) +# hyp = {'giou': 1.43, # giou loss gain +# 'xy': 4.688, # xy loss gain +# 'wh': 0.1857, # wh loss gain +# 'cls': 11.7, # cls loss gain +# 'cls_pw': 4.81, # cls BCELoss positive_weight +# 'obj': 11.5, # obj loss gain +# 'obj_pw': 1.56, # obj BCELoss positive_weight +# 'iou_t': 0.281, # iou training threshold +# 'lr0': 0.0013, # initial learning rate +# 'lrf': -4., # final LambdaLR learning rate = lr0 * (10 ** lrf) +# 'momentum': 0.944, # SGD momentum +# 'weight_decay': 0.000427, # optimizer weight decay +# 'hsv_s': 0.0599, # image HSV-Saturation augmentation (fraction) +# 'hsv_v': 0.142, # image HSV-Value augmentation (fraction) +# 'degrees': 1.03, # image rotation (+/- deg) +# 'translate': 0.0552, # image translation (+/- fraction) +# 'scale': 0.0555, # image scale (+/- gain) +# 'shear': 0.434} # image shear (+/- deg) def train(cfg,