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, etc. models to check how it predicts. Today, we will use Convolutional Neural Networks(CNN) VGG16 architecture pre-trained model to predict “Sunflower,” and we will check how much accuracy shows. We can also compare and see with ResNet50. VGG16 developed by Oxford Visual Geometry Group and they 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.
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2.71202862e-06, 3.62944138e-06, 2.47901971e-05, 3.23408221e-06,
5.97417511e-06, 1.55234098e-04, 1.26242821e-06, 4.08067339e-04,
3.11883724e-07, 9.41288832e-04, 1.89910679e-05, 4.65363119e-05,
6.62990264e-04, 2.46679643e-04, 1.15720606e-04, 1.27595180e-04,
3.17860258e-06, 2.90104672e-05, 8.44335300e-05, 5.12565236e-07,
8.89181763e-07, 3.85935891e-06, 8.37064290e-05, 1.64868208e-04,
3.51172348e-05, 5.60819490e-06, 2.19401511e-04, 4.08127438e-04,
2.69722318e-06, 5.18108427e-05, 1.41212431e-05, 5.73181251e-06,
1.92136831e-05, 4.39776253e-04, 1.11156645e-04, 5.55086626e-06,
1.17950176e-05, 1.13379328e-05, 7.41436179e-06, 4.34479443e-05,
5.60880426e-06, 2.54477936e-06, 2.78401558e-06, 2.52124846e-06,
4.74029133e-04, 1.11520667e-05, 1.58382863e-05, 2.21489008e-05,
8.94513869e-05, 4.53031425e-05, 2.42639339e-06, 9.41761471e-07,
2.41876455e-06, 2.39499968e-05, 1.76770504e-06, 3.49765382e-04,
1.04347237e-04, 1.82023689e-06, 2.56720523e-04, 8.46163039e-06,
2.87607800e-05, 1.36316055e-06, 7.28603163e-06, 5.31369369e-06,
3.09025025e-04, 6.92504454e-06, 1.13626294e-04, 6.45976834e-05,
2.12645959e-04, 6.34211210e-06, 5.79789173e-07, 1.00772922e-05,
2.38798311e-04, 2.79529445e-07, 2.56625754e-06, 1.54522166e-03,
4.59202738e-06, 8.97846530e-06, 1.08892273e-04, 1.85783074e-06,
2.58549233e-04, 1.10477367e-05, 4.26274755e-06, 2.16908393e-05,
5.18997585e-05, 2.66095361e-04, 7.00207602e-04, 3.97078747e-06,
8.14143732e-06, 4.05794941e-03, 4.56544990e-03, 1.36343551e-05,
9.84614326e-06, 2.23758383e-04, 1.06980542e-05, 7.60413613e-03,
3.89469869e-06, 1.54093863e-03, 2.07548455e-05, 1.38083542e-05,
1.64757068e-06, 4.22058765e-05, 3.34254612e-04, 7.71678606e-06,
2.17240336e-06, 8.05103082e-06, 3.34178258e-05, 9.74871909e-06,
4.19897819e-03, 2.21732148e-06, 2.94500951e-05, 1.44843875e-06,
4.40798671e-04, 1.36662566e-04, 9.72910811e-07, 1.83892189e-06,
3.00911506e-05, 4.90481125e-06, 1.06224115e-06, 1.51085749e-03,
2.16351000e-05, 1.75692949e-05, 3.48874892e-06, 8.44033882e-02,
5.02096314e-04, 8.81049375e-04, 7.91626280e-06, 2.65544746e-03,
1.60782315e-06, 1.75788991e-05, 4.40554885e-07, 3.00262604e-06,
8.78133532e-03, 1.14007060e-04, 2.35061689e-05, 3.09362349e-06,
3.14549688e-05, 9.27602650e-06, 5.56967512e-04, 1.34986755e-03,
4.28237363e-05, 8.68285861e-05, 6.86484273e-05, 2.41255839e-04,
1.01370015e-03, 8.49879652e-05, 1.17188638e-05, 3.81034974e-04,
2.95864129e-06, 2.00250111e-06, 1.92668776e-05, 4.98022244e-04,
3.20632651e-04, 1.25848544e-06, 4.34792355e-07, 5.19158457e-05,
2.88173087e-05, 3.58667467e-06, 2.74704871e-05, 8.08424229e-05,
4.49468207e-05, 3.96547621e-05, 6.18261356e-06, 5.46228403e-05,
1.21731800e-05, 6.00794783e-06, 7.07755646e-07, 9.70227939e-06,
2.10088911e-05, 1.33511290e-04, 7.22141749e-07, 5.04510172e-06,
1.28416696e-05, 6.84117026e-07, 5.20191236e-07, 2.89630179e-06,
1.45360449e-04, 1.05803738e-04, 9.16635327e-05, 4.01739671e-05,
7.56677173e-06, 4.95888307e-05, 9.37304321e-06, 3.22277687e-04,
1.61703036e-03, 1.06508247e-04, 4.11351398e-02, 3.95816169e-04,
2.24826828e-04, 2.55554100e-03, 1.74095680e-04, 1.36616029e-04,
9.14557095e-05, 8.98788494e-05, 2.36667966e-05, 6.75775846e-06,
8.64556787e-05, 5.90984455e-05, 1.71457632e-05, 2.27517444e-06,
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1.60261220e-03, 1.76478516e-05, 6.65154948e-06, 2.30248575e-03,
3.09689131e-06, 4.62867407e-04, 2.72313400e-06, 2.36071937e-04,
4.72994770e-06, 4.50090374e-06, 1.94501445e-05, 3.64630228e-06,
4.93342086e-05, 5.86665010e-07, 2.34285311e-04, 2.18613877e-06,
2.52503451e-05, 3.56117904e-01, 3.87917044e-06, 6.99442353e-06,
3.17362021e-04, 1.68024737e-03, 5.50244295e-04, 1.30264234e-04,
9.19837694e-05, 7.31236560e-06, 7.79973780e-05, 2.77132588e-03,
8.81705419e-05, 6.66962023e-06, 1.54279714e-05, 1.91835825e-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
[[('n11939491', 'daisy', 0.3561179),
('n02843684', 'birdhouse', 0.09260903),
('n04522168', 'vase', 0.08440339),
('n03991062', 'pot', 0.051201515),
('n07730033', 'cardoon', 0.04113514)]]
Wow! Correctly predicted as “Daisy” and 0.356 prediction accuracy.
Note: I have checked with ResNet50 with same sunflower image and shows as “Daisy” and 0.48 prediction accuracy.
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