From 407a4c481db4c295ce2607d6022612b3a2298fd4 Mon Sep 17 00:00:00 2001
From: Glenn Jocher <glenn.jocher@ultralytics.com>
Date: Wed, 17 Jul 2019 14:16:21 +0200
Subject: [PATCH] updates
---
train.py | 50 +++++++++++++++++++++++++-------------------------
1 file changed, 25 insertions(+), 25 deletions(-)
diff --git a/train.py b/train.py
index 66514ead..34982bab 100644
--- a/train.py
+++ b/train.py
@@ -21,34 +21,34 @@ from utils.utils import *
# 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 320 --epochs 2
-# # ha
-# hyp = {'giou': 0.8541, # giou loss gain
-# 'xy': 4.062, # xy loss gain
-# 'wh': 0.1845, # wh loss gain
-# 'cls': 21.61, # cls loss gain
-# 'cls_pw': 1.957, # cls BCELoss positive_weight
-# 'obj': 22.9, # obj loss gain
-# 'obj_pw': 2.894, # obj BCELoss positive_weight
-# 'iou_t': 0.3689, # iou target-anchor training threshold
-# 'lr0': 0.001844, # initial learning rate
-# 'lrf': -4., # final learning rate = lr0 * (10 ** lrf)
-# 'momentum': 0.913, # SGD momentum
-# 'weight_decay': 0.000467} # optimizer weight decay
-
-
-# hd
-hyp = {'giou': 1.153, # giou loss gain
+# Training hyperparameters a
+hyp = {'giou': 0.8541, # giou loss gain
'xy': 4.062, # xy loss gain
'wh': 0.1845, # wh loss gain
- 'cls': 24.28, # cls loss gain
- 'cls_pw': 3.05, # cls BCELoss positive_weight
- 'obj': 20.93, # obj loss gain
- 'obj_pw': 2.842, # obj BCELoss positive_weight
- 'iou_t': 0.2759, # iou target-anchor training threshold
- 'lr0': 0.001357, # initial learning rate
+ 'cls': 21.61, # cls loss gain
+ 'cls_pw': 1.957, # cls BCELoss positive_weight
+ 'obj': 22.9, # obj loss gain
+ 'obj_pw': 2.894, # obj BCELoss positive_weight
+ 'iou_t': 0.3689, # iou target-anchor training threshold
+ 'lr0': 0.001844, # initial learning rate
'lrf': -4., # final learning rate = lr0 * (10 ** lrf)
- 'momentum': 0.916, # SGD momentum
- 'weight_decay': 0.000572} # optimizer weight decay
+ 'momentum': 0.913, # SGD momentum
+ 'weight_decay': 0.000467} # optimizer weight decay
+
+
+# Training hyperparameters d
+# hyp = {'giou': 1.153, # giou loss gain
+# 'xy': 4.062, # xy loss gain
+# 'wh': 0.1845, # wh loss gain
+# 'cls': 24.28, # cls loss gain
+# 'cls_pw': 3.05, # cls BCELoss positive_weight
+# 'obj': 20.93, # obj loss gain
+# 'obj_pw': 2.842, # obj BCELoss positive_weight
+# 'iou_t': 0.2759, # iou target-anchor training threshold
+# 'lr0': 0.001357, # initial learning rate
+# 'lrf': -4., # final learning rate = lr0 * (10 ** lrf)
+# 'momentum': 0.916, # SGD momentum
+# 'weight_decay': 0.000572} # optimizer weight decay
def train(cfg,