Bienqq
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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2019-06-15 02:10:15 +02:00
parent bb3682024e
commit 02291622fa
2 changed files with 8 additions and 6 deletions
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12
train.py
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@ -12,11 +12,13 @@ from utils.datasets import *
from utils.utils import * from utils.utils import *
# Hyperparameters: train.py --evolve --epochs 2 --img-size 320, Metrics: 0.204 0.302 0.175 0.234 (square smart) # Hyperparameters: train.py --evolve --epochs 2 --img-size 320, Metrics: 0.204 0.302 0.175 0.234 (square smart)
hyp = {'xy': 0.1, # xy loss gain (giou is about 0.02) hyp = {'giou': .035, # giou loss gain
'wh': 0.1, # wh loss gain 'xy': 0.20, # xy loss gain
'cls': 0.04, # cls loss gain 'wh': 0.10, # wh loss gain
'conf': 4.5, # conf loss gain 'cls': 0.035, # cls loss gain
'iou_t': 0.5, # iou target-anchor training threshold 'conf': 1.61, # conf loss gain
'conf_bpw': 3.53, # conf BCELoss positive_weight
'iou_t': 0.29, # iou target-anchor training threshold
'lr0': 0.001, # initial learning rate 'lr0': 0.001, # initial learning rate
'lrf': -4., # final learning rate = lr0 * (10 ** lrf) 'lrf': -4., # final learning rate = lr0 * (10 ** lrf)
'momentum': 0.90, # SGD momentum 'momentum': 0.90, # SGD momentum

2
utils/utils.py
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@ -279,7 +279,7 @@ def compute_loss(p, targets, model): # predictions, targets, model
# Define criteria # Define criteria
MSE = nn.MSELoss() MSE = nn.MSELoss()
CE = nn.CrossEntropyLoss() # (weight=model.class_weights) CE = nn.CrossEntropyLoss() # (weight=model.class_weights)
BCE = nn.BCEWithLogitsLoss() BCE = nn.BCEWithLogitsLoss(pos_weight=ft([h['conf_bpw']]))
# Compute losses # Compute losses
bs = p[0].shape[0] # batch size bs = p[0].shape[0] # batch size