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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2019-04-18 12:27:28 +02:00
parent 8831913f10
commit f4dc0d84e4
1 changed files with 19 additions and 18 deletions
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train.py
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@ -11,14 +11,14 @@ from utils.datasets import *
from utils.utils import *
# Initialize hyperparameters
hyp = {'k': 8.4875, # loss multiple
'xy': 0.079756, # xy loss fraction
'wh': 0.010461, # wh loss fraction
'cls': 0.02105, # cls loss fraction
'conf': 0.88873, # conf loss fraction
'iou_t': 0.1, # iou target-anchor training threshold
'lr0': 0.001, # initial learning rate
'lrf': -2., # final learning rate = lr0 * (10 ** lrf)
hyp = {'k': 6.927, # loss multiple
'xy': 0.07556, # xy loss fraction
'wh': 0.008074, # wh loss fraction
'cls': 0.01113, # cls loss fraction
'conf': 0.9052, # conf loss fraction
'iou_t': 0.06154, # iou target-anchor training threshold
'lr0': 0.001136, # initial learning rate
'lrf': -2.52, # final learning rate = lr0 * (10 ** lrf)
'momentum': 0.9, # SGD momentum
'weight_decay': 0.0005, # optimizer weight decay
}
@ -89,7 +89,7 @@ def train(
# Scheduler (reduce lr at epochs 218, 245, i.e. batches 400k, 450k)
# lf = lambda x: 1 - x / epochs # linear ramp to zero
# lf = lambda x: 10 ** (-2 * x / epochs) # exp ramp to lr0 * 1e-2
lf = lambda x: 1 - 10 ** (-2 * (1 - x / epochs)) # inv exp ramp to lr0 * 1e-2
lf = lambda x: 1 - 10 ** (hyp['lrf'] * (1 - x / epochs)) # inv exp ramp to lr0 * 1e-2
scheduler = optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lf, last_epoch=start_epoch - 1)
# scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=[218, 245], gamma=0.1, last_epoch=start_epoch - 1)
@ -347,12 +347,13 @@ if __name__ == '__main__':
else:
hyp = old_hyp.copy() # reset hyp to
import numpy as np
import matplotlib.pyplot as plt
a = np.loadtxt('evolve.txt')
x = a[:,3]
fig = plt.figure(figsize=(14, 7))
for i in range(1,10):
plt.subplot(2,5,i)
plt.plot(x,a[:,i+5],'.')
# # Plot results
# import numpy as np
# import matplotlib.pyplot as plt
#
# a = np.loadtxt('evolve.txt')
# x = a[:, 3]
# fig = plt.figure(figsize=(14, 7))
# for i in range(1, 10):
# plt.subplot(2, 5, i)
# plt.plot(x, a[:, i + 5], '.')