Image Source: Pexels
We have already gone through and , one of the blogs. We don’t need to create a new architecture from scratch for our projects; instead, we can use existing Convolutional Neural Networks(CNN) popular architectures like LeNet-5, ResNet, VGG16, VGG19, etc. models to check how it predicts. Today, we will use Convolutional Neural Networks(CNN) VGG19 architecture pre-trained model to predict “Alps Mountain” and check how much accuracy shows. We can also compare and see with ResNet50. VGG16 by Oxford Visual Geometry Group and won the ILSVRC challenge in 2016.
I am working on a few kinds of research on the Enterprise Resource Planning side and using Convolutional Neural Networks(CNN) Image Recognition and computer vision.
I have used Google Colab IDE for this example, and you can download it from the GitHub Repository.
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1.96746932e-05, 1.05146705e-06, 7.00899909e-05, 6.10502275e-06,
5.09750207e-05, 1.31850061e-06, 3.05437356e-06, 7.18731008e-06,
3.09868653e-07, 5.41049394e-06, 3.55704333e-06, 3.81433665e-07,
1.52391485e-05, 5.54605231e-06, 6.60281273e-07, 1.55462723e-04,
1.44475362e-05, 2.66315737e-06, 3.34570217e-07, 7.32056214e-05,
1.39755766e-05, 7.35772687e-07, 2.02945695e-04, 2.14583688e-05,
2.78108905e-06, 4.81601728e-06, 2.28538715e-06, 4.08594497e-06,
9.40966288e-07, 3.12678822e-06, 8.10134964e-08, 3.63377490e-06,
3.31193442e-05, 1.09878099e-04, 5.86889044e-04, 1.09050576e-04,
2.43393661e-04, 8.57719351e-05, 2.62104749e-07, 9.85947827e-06,
2.64415689e-06, 2.17324214e-05, 1.98278110e-04, 3.76144308e-04,
8.40111397e-07, 3.16844944e-06, 4.39692449e-05, 3.60804370e-05,
4.70703480e-06, 4.41952068e-07, 1.32872316e-07, 6.12612666e-07,
1.05658655e-04, 1.12266321e-06, 7.57072166e-06, 1.47980085e-04,
2.08926804e-06, 5.17502599e-07, 4.57661285e-04, 5.11424500e-04,
3.15348989e-05, 5.16414075e-05, 4.09983249e-06, 2.04137905e-07,
1.18675533e-04, 1.84740900e-04, 2.22259564e-06, 1.19537044e-05,
1.06769016e-06, 8.08187760e-05, 2.77156801e-06, 5.68456526e-05,
1.14128496e-02, 2.39847886e-05, 5.46495494e-06, 1.74064837e-06,
1.00392375e-04, 2.41997768e-05, 1.04285327e-05, 1.93671905e-03,
2.96456055e-05, 2.15976470e-05, 2.87566036e-06, 8.44919487e-05,
1.23012978e-05, 3.83619990e-05, 1.25679162e-05, 1.14992408e-05,
4.76936293e-07, 6.23678352e-06, 1.54639383e-06, 1.01053440e-04,
7.49499713e-06, 2.61491485e-04, 1.56690262e-03, 4.08316373e-06,
1.11798988e-06, 1.66422426e-04, 1.07789398e-04, 1.02873082e-05,
2.29807847e-06, 3.05756475e-06, 2.19733265e-04, 2.81367596e-04,
1.34871800e-06, 3.72290356e-06, 4.31499029e-05, 4.36455084e-05,
1.77590991e-05, 2.13052181e-06, 5.73515649e-07, 3.55046541e-06,
5.17432287e-04, 1.49896432e-05, 4.62287176e-07, 1.22886877e-06,
3.89151937e-05, 4.31168883e-06, 3.16389134e-07, 7.24991143e-04,
1.59541250e-05, 3.60390504e-06, 2.45808792e-06, 3.58678176e-06,
3.45878689e-05, 1.97894915e-04, 8.58600379e-06, 5.45678704e-05,
1.54423364e-03, 2.88227147e-06, 6.96886048e-07, 3.80836127e-07,
1.09409066e-05, 5.40099791e-06, 2.65079211e-06, 6.66153664e-06,
1.15508374e-06, 1.15016030e-06, 4.98223999e-05, 9.13794884e-06,
9.50735648e-06, 2.90591231e-07, 4.62982280e-06, 8.32266160e-05,
6.88465661e-04, 1.42698584e-04, 2.73367874e-07, 4.82496616e-05,
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4.48798615e-04, 1.04029699e-04, 7.13986651e-07, 1.40612639e-04,
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2.10096085e-04, 3.99710843e-05, 1.54138434e-05, 2.30619880e-05,
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3.34645229e-05, 9.61714250e-06, 3.97372642e-05, 2.34125779e-04,
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7.61495721e-06, 2.72218676e-06, 3.08668584e-01, 4.34981630e-04,
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1.93409815e-05, 6.72790047e-05, 8.78218634e-05, 7.81626222e-05,
3.99465462e-05, 4.28662759e-07, 2.78910619e-07, 4.62737307e-06,
2.33212886e-05, 5.22128539e-06, 5.44130635e-05, 1.49917887e-05]],
dtype=float32)
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Downloading data from https://storage.googleapis.com/download.tensorflow.org/data/imagenet_class_index.json 40960/35363 [==================================] - 0s 0us/step 49152/35363 [=========================================] - 0s 0us/step
[[('n09193705', 'alp', 0.30866858),
('n09468604', 'valley', 0.2623897),
('n03388043', 'fountain', 0.12551005),
('n02980441', 'castle', 0.06944967),
('n09472597', 'volcano', 0.050923396)]]
Wow! Correctly predicted as “Alps Mountain” and 0.308 prediction accuracy.
Let’s check the prediction accuracy on VGG16 and ResNet50.
VGG16:
[[('n09193705', 'alp', 0.32918993),
('n09468604', 'valley', 0.32035673),
('n09246464', 'cliff', 0.08723663),
('n03388043', 'fountain', 0.060651984),
('n01943899', 'conch', 0.024463423)]]
ResNet50:
[[('n03388043', 'fountain', 0.86726224),
('n09193705', 'alp', 0.029720154),
('n09468604', 'valley', 0.02816105),
('n03792972', 'mountain_tent', 0.016437855),
('n02948072', 'candle', 0.009712413)]]
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