Image dimensions : 150x250

gistfile1.txt

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)


model = Sequential()
model.add(Conv2D(128, (7, 7), padding = 'same', input_shape=input_shape))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.5))

model.add(Conv2D(128, (7, 7), padding = 'same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.6))

model.add(Conv2D(128, (7, 7), padding = 'same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.6))

model.add(Conv2D(128, (7, 7), padding = 'same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.6))

model.add(Conv2D(64, (7, 7), padding = 'same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.6))

model.add(Conv2D(64, (7, 7), padding = 'same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.6))

model.add(Conv2D(32, (7, 7), padding = 'same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.6))

model.add(Flatten())
model.add(Dense(32))
model.add(Activation('relu'))
model.add(Dense(1))
model.add(Activation('sigmoid'))

model.compile(loss='binary_crossentropy',
              optimizer=optimizers.Adam(lr=3e-5),
              metrics=['accuracy'])

model.summary()


*************************************************************************************

Found 29124 images belonging to 2 classes.
Found 10401 images belonging to 2 classes.
Epoch 1/80
910/910 [==============================] - 774s 851ms/step - loss: 0.6948 - acc: 0.5040 - val_loss: 0.6931 - val_acc: 0.4954
Epoch 2/80
910/910 [==============================] - 757s 832ms/step - loss: 0.6810 - acc: 0.5319 - val_loss: 0.6100 - val_acc: 0.6791
Epoch 3/80
910/910 [==============================] - 750s 824ms/step - loss: 0.4800 - acc: 0.7983 - val_loss: 0.3779 - val_acc: 0.8695
Epoch 4/80
910/910 [==============================] - 748s 821ms/step - loss: 0.3478 - acc: 0.8668 - val_loss: 0.3041 - val_acc: 0.9014
Epoch 5/80
910/910 [==============================] - 746s 820ms/step - loss: 0.2798 - acc: 0.8961 - val_loss: 0.2427 - val_acc: 0.9308
Epoch 6/80
910/910 [==============================] - 746s 820ms/step - loss: 0.2417 - acc: 0.9109 - val_loss: 0.2626 - val_acc: 0.9008
Epoch 7/80
910/910 [==============================] - 750s 825ms/step - loss: 0.2214 - acc: 0.9190 - val_loss: 0.2099 - val_acc: 0.9315
Epoch 8/80
910/910 [==============================] - 743s 817ms/step - loss: 0.2041 - acc: 0.9248 - val_loss: 0.2100 - val_acc: 0.9215
Epoch 9/80
910/910 [==============================] - 745s 819ms/step - loss: 0.1988 - acc: 0.9292 - val_loss: 0.1748 - val_acc: 0.9364
Epoch 10/80
910/910 [==============================] - 746s 820ms/step - loss: 0.1870 - acc: 0.9332 - val_loss: 0.1740 - val_acc: 0.9405
Epoch 11/80
910/910 [==============================] - 742s 815ms/step - loss: 0.1750 - acc: 0.9376 - val_loss: 0.1486 - val_acc: 0.9479
Epoch 12/80
910/910 [==============================] - 743s 816ms/step - loss: 0.1714 - acc: 0.9375 - val_loss: 0.1717 - val_acc: 0.9343
Epoch 13/80
910/910 [==============================] - 745s 819ms/step - loss: 0.1686 - acc: 0.9390 - val_loss: 0.1544 - val_acc: 0.9473
Epoch 14/80
910/910 [==============================] - 736s 809ms/step - loss: 0.1636 - acc: 0.9415 - val_loss: 0.1685 - val_acc: 0.9335
Epoch 15/80
910/910 [==============================] - 750s 825ms/step - loss: 0.1582 - acc: 0.9432 - val_loss: 0.1348 - val_acc: 0.9472
Epoch 16/80
910/910 [==============================] - 738s 811ms/step - loss: 0.1531 - acc: 0.9436 - val_loss: 0.1428 - val_acc: 0.9537
Epoch 17/80
910/910 [==============================] - 743s 817ms/step - loss: 0.1502 - acc: 0.9458 - val_loss: 0.1478 - val_acc: 0.9420
Epoch 18/80
910/910 [==============================] - 753s 827ms/step - loss: 0.1465 - acc: 0.9467 - val_loss: 0.1413 - val_acc: 0.9531
Epoch 19/80
910/910 [==============================] - 754s 829ms/step - loss: 0.1447 - acc: 0.9474 - val_loss: 0.1236 - val_acc: 0.9557
Epoch 20/80
910/910 [==============================] - 752s 826ms/step - loss: 0.1389 - acc: 0.9484 - val_loss: 0.1373 - val_acc: 0.9511
Epoch 21/80
910/910 [==============================] - 751s 826ms/step - loss: 0.1347 - acc: 0.9507 - val_loss: 0.1577 - val_acc: 0.9503
Epoch 22/80
910/910 [==============================] - 761s 836ms/step - loss: 0.1419 - acc: 0.9495 - val_loss: 0.1312 - val_acc: 0.9558
Epoch 23/80
910/910 [==============================] - 759s 834ms/step - loss: 0.1334 - acc: 0.9518 - val_loss: 0.1168 - val_acc: 0.9569
Epoch 24/80
910/910 [==============================] - 755s 830ms/step - loss: 0.1327 - acc: 0.9508 - val_loss: 0.1227 - val_acc: 0.9551
Epoch 25/80
910/910 [==============================] - 759s 834ms/step - loss: 0.1321 - acc: 0.9522 - val_loss: 0.1400 - val_acc: 0.9553
Epoch 26/80
910/910 [==============================] - 765s 840ms/step - loss: 0.1256 - acc: 0.9531 - val_loss: 0.1450 - val_acc: 0.9473
Epoch 27/80
910/910 [==============================] - 760s 835ms/step - loss: 0.1273 - acc: 0.9546 - val_loss: 0.1350 - val_acc: 0.9585
Epoch 28/80
910/910 [==============================] - 761s 836ms/step - loss: 0.1273 - acc: 0.9528 - val_loss: 0.1412 - val_acc: 0.9578
Epoch 29/80
910/910 [==============================] - 760s 835ms/step - loss: 0.1227 - acc: 0.9537 - val_loss: 0.1226 - val_acc: 0.9584
Epoch 30/80
910/910 [==============================] - 761s 836ms/step - loss: 0.1197 - acc: 0.9553 - val_loss: 0.1231 - val_acc: 0.9577
Epoch 31/80
910/910 [==============================] - 759s 834ms/step - loss: 0.1212 - acc: 0.9562 - val_loss: 0.1432 - val_acc: 0.9544
Epoch 32/80
910/910 [==============================] - 755s 829ms/step - loss: 0.1210 - acc: 0.9565 - val_loss: 0.1329 - val_acc: 0.9563
Epoch 33/80
910/910 [==============================] - 757s 832ms/step - loss: 0.1177 - acc: 0.9562 - val_loss: 0.1185 - val_acc: 0.9596
Epoch 34/80
910/910 [==============================] - 758s 833ms/step - loss: 0.1181 - acc: 0.9558 - val_loss: 0.1213 - val_acc: 0.9585
Epoch 35/80
910/910 [==============================] - 751s 825ms/step - loss: 0.1154 - acc: 0.9572 - val_loss: 0.1235 - val_acc: 0.9596
Epoch 36/80
910/910 [==============================] - 752s 826ms/step - loss: 0.1151 - acc: 0.9569 - val_loss: 0.1263 - val_acc: 0.9591
Epoch 37/80
910/910 [==============================] - 754s 828ms/step - loss: 0.1111 - acc: 0.9594 - val_loss: 0.1140 - val_acc: 0.9597
Epoch 38/80
910/910 [==============================] - 750s 824ms/step - loss: 0.1129 - acc: 0.9582 - val_loss: 0.1086 - val_acc: 0.9596
Epoch 39/80
910/910 [==============================] - 759s 834ms/step - loss: 0.1115 - acc: 0.9588 - val_loss: 0.1204 - val_acc: 0.9606
Epoch 40/80
910/910 [==============================] - 760s 835ms/step - loss: 0.1112 - acc: 0.9586 - val_loss: 0.1205 - val_acc: 0.9605
Epoch 41/80
910/910 [==============================] - 759s 834ms/step - loss: 0.1111 - acc: 0.9591 - val_loss: 0.1245 - val_acc: 0.9599
Epoch 42/80
910/910 [==============================] - 758s 833ms/step - loss: 0.1101 - acc: 0.9588 - val_loss: 0.1118 - val_acc: 0.9612
Epoch 43/80
910/910 [==============================] - 756s 831ms/step - loss: 0.1081 - acc: 0.9604 - val_loss: 0.1417 - val_acc: 0.9570
Epoch 44/80
910/910 [==============================] - 754s 829ms/step - loss: 0.1056 - acc: 0.9607 - val_loss: 0.1179 - val_acc: 0.9588
Epoch 45/80
910/910 [==============================] - 762s 837ms/step - loss: 0.1059 - acc: 0.9612 - val_loss: 0.1300 - val_acc: 0.9608
Epoch 46/80
910/910 [==============================] - 762s 837ms/step - loss: 0.1059 - acc: 0.9612 - val_loss: 0.1519 - val_acc: 0.9520
Epoch 47/80
910/910 [==============================] - 753s 828ms/step - loss: 0.1085 - acc: 0.9598 - val_loss: 0.1666 - val_acc: 0.9466
Epoch 48/80
910/910 [==============================] - 756s 831ms/step - loss: 0.1035 - acc: 0.9605 - val_loss: 0.1309 - val_acc: 0.9603
Epoch 49/80
910/910 [==============================] - 758s 833ms/step - loss: 0.1022 - acc: 0.9629 - val_loss: 0.1233 - val_acc: 0.9597
Epoch 50/80
910/910 [==============================] - 759s 834ms/step - loss: 0.1023 - acc: 0.9618 - val_loss: 0.1414 - val_acc: 0.9562
Epoch 51/80
910/910 [==============================] - 766s 841ms/step - loss: 0.1042 - acc: 0.9622 - val_loss: 0.1421 - val_acc: 0.9565
Epoch 52/80
910/910 [==============================] - 757s 832ms/step - loss: 0.1020 - acc: 0.9616 - val_loss: 0.1315 - val_acc: 0.9580
Epoch 53/80
910/910 [==============================] - 754s 829ms/step - loss: 0.0999 - acc: 0.9618 - val_loss: 0.1251 - val_acc: 0.9606
Epoch 54/80
910/910 [==============================] - 757s 831ms/step - loss: 0.1022 - acc: 0.9620 - val_loss: 0.1216 - val_acc: 0.9610
Epoch 55/80
910/910 [==============================] - 758s 833ms/step - loss: 0.1000 - acc: 0.9626 - val_loss: 0.1342 - val_acc: 0.9581
Epoch 56/80
910/910 [==============================] - 758s 832ms/step - loss: 0.1013 - acc: 0.9633 - val_loss: 0.1209 - val_acc: 0.9599