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// The model overfits around 20 epochs. The saved checkpoint model works well on the test set by giving just 18 false positives | |
// as compared to 36 in other cases. The model is shallow as compared to other researchers. The batch size=128 and small lr | |
// were significant factor in getting low losses. Next try would be to increase lr and dropout. | |
// This was trained on the new dataset | |
train_data_path = 'detection_data_1/train/' | |
validation_data_path = 'detection_data_1/val/' | |
test_data_path = 'detection_data_1/test/' |
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// The val acc. does not increase beyond 95% and the training acc. somewhat seems to get lower gradually. Without reducing | |
// the lr there is a possibility to converge. | |
train_data_path = 'detection_data_1/train/' | |
validation_data_path = 'detection_data_1/val/' | |
test_data_path = 'detection_data_1/test/' | |
#Parametres | |
img_width, img_height = 150, 250 |
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img_width, img_height = 300, 500 | |
********************************************************* | |
if K.image_data_format() == 'channels_first': | |
input_shape = (3, img_width, img_height) | |
else: | |
input_shape = (img_width, img_height, 3) | |
model = Sequential() |
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img_width, img_height = 200, 300 | |
********************************************************* | |
if K.image_data_format() == 'channels_first': | |
input_shape = (3, img_width, img_height) | |
else: | |
input_shape = (img_width, img_height, 3) | |
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img_width, img_height = 150, 250 | |
********************************************************* | |
if K.image_data_format() == 'channels_first': | |
input_shape = (3, img_width, img_height) | |
else: | |
input_shape = (img_width, img_height, 3) | |