updates
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train.py
2
train.py
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@ -12,7 +12,7 @@ from utils.datasets import *
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from utils.utils import *
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from utils.utils import *
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# Hyperparameters: train.py --evolve --epochs 2 --img-size 320, Metrics: 0.204 0.302 0.175 0.234 (square smart)
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# Hyperparameters: train.py --evolve --epochs 2 --img-size 320, Metrics: 0.204 0.302 0.175 0.234 (square smart)
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hyp = {'xy': 0.2, # xy loss gain
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hyp = {'xy': 0.2, # xy loss gain (giou is about 0.02)
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'wh': 0.1, # wh loss gain
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'wh': 0.1, # wh loss gain
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'cls': 0.04, # cls loss gain
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'cls': 0.04, # cls loss gain
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'conf': 4.5, # conf loss gain
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'conf': 4.5, # conf loss gain
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@ -230,7 +230,7 @@ class LoadImagesAndLabels(Dataset): # for training/testing
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augment_hsv = True
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augment_hsv = True
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if self.augment and augment_hsv:
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if self.augment and augment_hsv:
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# SV augmentation by 50%
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# SV augmentation by 50%
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fraction = 0.25 # must be < 1.0
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fraction = 0.50 # must be < 1.0
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img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) # hue, sat, val
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img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) # hue, sat, val
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S = img_hsv[:, :, 1].astype(np.float32) # saturation
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S = img_hsv[:, :, 1].astype(np.float32) # saturation
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V = img_hsv[:, :, 2].astype(np.float32) # value
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V = img_hsv[:, :, 2].astype(np.float32) # value
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@ -269,7 +269,7 @@ class LoadImagesAndLabels(Dataset): # for training/testing
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# Augment image and labels
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# Augment image and labels
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if self.augment:
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if self.augment:
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img, labels = random_affine(img, labels, degrees=(-5, 5), translate=(0.0, 0.0), scale=(1.0, 1.0))
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img, labels = random_affine(img, labels, degrees=(-5, 5), translate=(0.10, 0.10), scale=(0.90, 1.10))
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nL = len(labels) # number of labels
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nL = len(labels) # number of labels
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if nL:
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if nL:
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