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, InceptionV3, etc. models to check how it predicts. Today, we will use Convolutional Neural Networks(CNN) InceptionV3 architecture pre-trained model to predict “Boathouse” and check how much accuracy shows. We can also compare and see with ResNet50. VGG16, and VGG19 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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2.13409076e-03, 3.07763039e-05, 8.36677646e-05, 1.54462585e-04,
1.91476342e-04, 4.74414264e-05, 1.27004154e-04, 1.06490341e-04,
3.40374594e-04, 2.05418328e-04, 6.30491078e-02, 1.74127505e-04,
2.11639286e-04, 4.23357487e-05, 1.83412863e-04, 1.40167860e-04,
1.08570792e-03, 8.06384196e-05, 7.04120612e-05, 2.13136402e-04,
1.15736904e-04, 2.24936433e-04, 5.14763124e-05, 1.31694236e-04,
1.31897221e-04, 2.76294479e-04, 1.09983499e-04, 1.82928547e-04,
1.63801582e-04, 1.86170029e-04, 1.09683409e-04, 7.52682099e-05,
1.27670064e-04, 1.68004393e-04, 3.77346441e-04, 9.02212123e-05,
7.51624102e-05, 3.03664765e-05, 2.53214021e-05, 9.76604642e-05,
6.54539908e-05, 6.95340204e-05, 8.46595067e-05, 9.48742090e-05,
6.79778968e-05, 8.12707149e-05, 1.97808095e-05, 4.65995327e-05,
2.11873834e-04, 9.51794354e-05, 2.06573881e-04, 6.00450039e-05,
7.71858977e-05, 1.37436058e-04, 1.84355804e-03, 1.09793706e-04,
3.92082329e-05, 1.60241601e-04, 1.93467436e-04, 1.00398545e-04,
7.75115041e-05, 6.31864023e-05, 7.64682482e-05, 1.15285358e-04,
6.81872916e-05, 4.44926700e-04, 7.33536654e-05, 5.19258552e-04,
7.05507991e-05, 1.23141624e-04, 1.41262222e-04, 8.16999163e-05,
5.86912734e-03, 2.49704230e-04, 1.45184735e-04, 2.63233633e-05,
4.40183503e-05, 1.66982747e-04, 1.35884169e-04, 5.94450394e-05,
8.83552639e-05, 8.50891447e-05, 6.42418236e-05, 1.35753406e-04,
2.70365163e-05, 8.68851275e-05, 7.69264952e-05, 7.63451026e-05,
4.99878515e-05, 8.29135606e-05, 1.54687834e-04, 1.97556510e-04,
2.15304593e-04, 1.89018465e-04, 1.85933066e-04, 9.14589036e-05,
1.15790332e-04, 9.59920071e-05, 1.88824168e-04, 2.81514804e-04,
2.11245570e-04, 8.64429530e-05, 4.86377976e-05, 8.99740116e-05,
2.34432169e-04, 2.70986115e-04, 4.42890218e-03, 7.80090995e-05,
7.11929533e-05, 4.60458723e-05, 2.65505136e-04, 4.30141255e-04,
1.85298981e-04, 5.14478469e-03, 2.00506503e-04, 4.58142022e-05,
1.32883535e-04, 7.02303223e-05, 1.22115409e-04, 1.72633416e-04,
6.95762792e-05, 5.81885688e-04, 3.67187422e-05, 2.25242955e-04,
8.08661396e-04, 2.20039813e-03, 1.54953901e-04, 5.92340948e-04,
2.31951344e-04, 6.39193386e-05, 3.80235288e-04, 1.23591814e-03,
1.35089344e-04, 2.60646309e-04, 9.75705334e-05, 2.35377214e-04,
1.37022507e-04, 1.18114753e-04, 2.98310391e-04, 1.02191530e-04,
8.94897457e-05, 4.76151581e-05, 6.07858856e-05, 7.11279426e-05,
1.05035208e-04, 7.99374771e-04, 2.19108188e-04, 6.51957598e-05,
1.39598895e-04, 4.47719154e-04, 9.81485005e-04, 1.61101911e-04,
4.87516430e-04, 3.26192618e-04, 7.29328312e-05, 2.64085160e-04,
7.35988506e-05, 9.75292278e-05, 4.46013655e-05, 4.32591878e-05,
2.33176179e-04, 1.67922626e-04, 7.83864452e-05, 1.95596996e-03,
7.66113881e-05, 5.06676151e-04, 1.60827185e-04, 1.35818424e-04,
4.08470922e-04, 1.34132002e-04, 9.88665124e-05, 1.24618426e-04,
8.10063138e-05, 1.51746615e-04, 6.29837814e-05, 4.15148024e-05,
8.91927746e-04, 8.94289115e-05, 1.00490783e-04, 3.83034640e-04,
2.24972682e-04, 1.55558228e-04, 1.77595139e-04, 2.77769082e-04,
1.71616426e-04, 2.47408498e-05, 4.73287182e-05, 5.45402581e-05,
9.18308724e-05, 1.21356949e-04, 4.09687549e-04, 1.91560524e-04,
1.65631296e-04, 2.76262581e-04, 5.11135331e-05, 2.00243841e-04,
1.56143084e-04, 2.20407252e-04, 1.76982488e-04, 1.38699237e-04,
1.38782198e-04, 2.46689800e-04, 1.22134981e-04, 1.04719446e-04,
8.74271354e-05, 2.98329600e-04, 9.90920234e-04, 4.02164820e-04,
1.26431827e-04, 4.36491027e-05, 1.44237929e-04, 1.56473499e-04,
2.01803210e-04, 1.17284137e-04, 1.19987140e-04, 1.98788082e-04,
1.06318337e-04, 1.60879965e-04, 2.72858568e-04, 9.27639048e-05,
9.63252533e-05, 1.27261621e-04, 1.16266805e-04, 1.28503685e-04,
2.16760149e-04, 1.80001502e-04, 1.60492113e-04, 1.24236030e-04,
9.36595898e-05, 7.42628690e-05, 1.10512992e-04, 7.72708954e-05,
2.84064561e-04, 4.29948923e-05, 1.46841150e-04, 6.16762118e-05,
5.84873305e-05, 4.42846285e-05, 7.80405026e-05, 3.39752238e-04,
7.10089298e-05, 6.12426011e-05, 4.87278667e-05, 1.07702668e-04,
4.01234902e-05, 8.03979128e-05, 6.16903271e-05, 5.81298846e-05,
5.20018984e-05, 1.01056270e-04, 5.90567324e-05, 2.39513582e-04,
2.03253745e-04, 1.04323917e-04, 1.04778075e-04, 1.35995666e-04,
2.50499084e-04, 1.62745549e-04, 7.43418786e-05, 9.07244103e-05,
7.44189165e-05, 2.03419942e-04, 1.39388896e-04, 9.45961146e-05,
3.52874777e-04, 3.38318438e-04, 6.15427198e-05, 3.68765220e-02,
3.69821239e-04, 7.43500772e-04, 4.06701770e-03, 2.47952150e-04,
1.34243135e-04, 1.30182350e-04, 2.91582808e-04, 1.84897406e-04,
2.16834800e-04, 1.22418918e-04, 9.65665167e-05, 1.17880569e-04,
1.30058004e-04, 8.96655547e-05, 4.02025034e-05, 1.91704734e-04,
1.48628038e-04, 8.97410064e-05, 7.33409397e-05, 2.48315831e-04,
1.64935846e-04, 1.48335079e-04, 1.73286273e-04, 8.32612568e-05]],
dtype=float32)
Copy to Clipboard
[[('n03216828', 'dock', 0.36978266),
('n02859443', 'boathouse', 0.25278783),
('n03933933', 'pier', 0.06304911),
('n09332890', 'lakeside', 0.036876522),
('n03877845', 'palace', 0.01331465)]]
Wow! it predicted two things here as “dock” with 0.369 prediction accuracy and “boathouse” with 0.25 prediction accuracy.
Let’s check the prediction accuracy on VGG19, VGG16 and ResNet50.
[('n03216828', 'dock', 0.6446606),
('n03447447', 'gondola', 0.18577594),
('n03933933', 'pier', 0.03848932),
('n02894605', 'breakwater', 0.029645536),
('n02859443', 'boathouse', 0.027358243)]]
VGG19:
[[('n03447447', 'gondola', 0.41522947),
('n03216828', 'dock', 0.2927551),
('n02859443', 'boathouse', 0.12971269),
('n09332890', 'lakeside', 0.0425403),
('n03095699', 'container_ship', 0.021784496)]]
[[('n03216828', 'dock', 0.7621628),
('n03947888', 'pirate', 0.065857835),
('n03095699', 'container_ship', 0.033779334),
('n04147183', 'schooner', 0.012518705),
('n03673027', 'liner', 0.010020729)]]
In this example, InceptionV3 pretrained model predicts better than above three models.
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