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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2019-07-17 14:14:42 +02:00
parent 34ddceea89
commit 33838b558d
2 changed files with 36 additions and 20 deletions
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43
train.py
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@ -15,25 +15,40 @@ from utils.utils import *
# 0.223 0.218 0.138 0.189 9.28 1.153 4.376 0.08263 24.28 3.05 20.93 2.842 0.2759 0.001357 -5.036 0.9158 0.0005722 mAP/F1 - 50/50 weighting # 0.223 0.218 0.138 0.189 9.28 1.153 4.376 0.08263 24.28 3.05 20.93 2.842 0.2759 0.001357 -5.036 0.9158 0.0005722 mAP/F1 - 50/50 weighting
# 0.231 0.215 0.135 0.191 9.51 1.432 3.007 0.06082 24.87 3.477 24.13 2.802 0.3436 0.001127 -5.036 0.9232 0.0005874 # 0.231 0.215 0.135 0.191 9.51 1.432 3.007 0.06082 24.87 3.477 24.13 2.802 0.3436 0.001127 -5.036 0.9232 0.0005874
# 0.246 0.194 0.128 0.192 8.12 1.101 3.954 0.0817 22.83 3.967 19.83 1.779 0.3352 0.000895 -5.036 0.9238 0.0007973 # 0.246 0.194 0.128 0.192 8.12 1.101 3.954 0.0817 22.83 3.967 19.83 1.779 0.3352 0.000895 -5.036 0.9238 0.0007973
# 0.242 0.296 0.196 0.231 5.67 0.8541 4.286 0.1539 21.61 1.957 22.9 2.894 0.3689 0.001844 -4 0.913 0.000467 # 0.242 0.296 0.196 0.231 5.67 0.8541 4.286 0.1539 21.61 1.957 22.9 2.894 0.3689 0.001844 -4 0.913 0.000467 # ha
# 0.298 0.244 0.167 0.247 4.99 0.8896 4.067 0.1694 21.41 2.033 25.61 1.783 0.4115 0.00128 -4 0.950 0.000377 # 0.298 0.244 0.167 0.247 4.99 0.8896 4.067 0.1694 21.41 2.033 25.61 1.783 0.4115 0.00128 -4 0.950 0.000377 # hb
# 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 # 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 # 320 --epochs 2 # 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
# Training hyperparameters # # ha
hyp = {'giou': 0.8541, # giou loss gain # 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
'xy': 4.062, # xy loss gain 'xy': 4.062, # xy loss gain
'wh': 0.1845, # wh loss gain 'wh': 0.1845, # wh loss gain
'cls': 21.61, # cls loss gain 'cls': 24.28, # cls loss gain
'cls_pw': 1.957, # cls BCELoss positive_weight 'cls_pw': 3.05, # cls BCELoss positive_weight
'obj': 22.9, # obj loss gain 'obj': 20.93, # obj loss gain
'obj_pw': 2.894, # obj BCELoss positive_weight 'obj_pw': 2.842, # obj BCELoss positive_weight
'iou_t': 0.3689, # iou target-anchor training threshold 'iou_t': 0.2759, # iou target-anchor training threshold
'lr0': 0.001844, # initial learning rate 'lr0': 0.001357, # initial learning rate
'lrf': -4., # final learning rate = lr0 * (10 ** lrf) 'lrf': -4., # final learning rate = lr0 * (10 ** lrf)
'momentum': 0.913, # SGD momentum 'momentum': 0.916, # SGD momentum
'weight_decay': 0.000467} # optimizer weight decay 'weight_decay': 0.000572} # optimizer weight decay
def train(cfg, def train(cfg,

13
utils/datasets.py
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@ -277,12 +277,13 @@ class LoadImagesAndLabels(Dataset): # for training/testing
if img is None: if img is None:
img = cv2.imread(img_path) # BGR img = cv2.imread(img_path) # BGR
assert img is not None, 'File Not Found ' + img_path assert img is not None, 'File Not Found ' + img_path
if self.n < 2001: r = self.img_size / max(img.shape) # size ratio
r = self.img_size / max(img.shape) # ratio if r < 1: # downsize is target shape is smaller
if r < 1: # downsize image and cache in RAM for future h, w, _ = img.shape
h, w, _ = img.shape img = cv2.resize(img, (int(w * r), int(h * r)), interpolation=cv2.INTER_AREA)
img = cv2.resize(img, (int(w*r), int(h*r)), interpolation=cv2.INTER_AREA) # resized, no border
self.imgs[index] = img # cache image into memory if self.n < 3000: # cache images into memory if image count < 3000
self.imgs[index] = img
# Augment colorspace # Augment colorspace
augment_hsv = True augment_hsv = True