Xception architecture is similar to InceptionV3; however, the model parameters are efficiently used in Xception. This is another pretrained model in
To know about the Xception, you can visit the research paper “Xception: Deep Learning with Depthwise Separable Convolutions.”
Image Source: Pexels
We have already gone through , Predict An Image Using InceptionV3 Pretrained Model, Predict an Image Using MobileNetV3 Pretrained Model for Mobile, and in the previous 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, MobileNetV3, NASNetMobile, etc. models to check how it predicts.
Today, we will use Convolutional Neural Networks(CNN) Xception architecture pre-trained model to predict “Sea Lion” and check how much accuracy shows.
We will compare Xception with ResNet50. VGG16 and VGG19 by Oxford Visual Geometry Group, MobileNetV3, NASNetMobile, and InceptionV3.
I am working on a few kinds of research projects 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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5.63239919e-05, 1.92445877e-05, 4.61178206e-06, 1.09173943e-05,
4.78988159e-06, 8.45852719e-06, 1.84542369e-05, 2.50549056e-05,
9.64706123e-06, 2.32298703e-06, 1.12564976e-05, 7.22398499e-06,
1.50206775e-04, 9.17318084e-06, 5.85046037e-05, 2.32787825e-05,
6.10604775e-06, 2.27048804e-05, 1.39018803e-05, 2.15326925e-03,
1.89266884e-05, 3.27023226e-05, 1.78439859e-05, 1.51578797e-05,
7.46315800e-06, 7.98816254e-05, 1.90593221e-06, 1.86640264e-05,
9.82744459e-05, 7.51531752e-06, 4.06048957e-06, 2.59392364e-05,
2.19849699e-05, 2.76014835e-05, 8.85916143e-06, 1.26045152e-05,
3.72829732e-06, 1.54448026e-05, 2.15991731e-05, 3.10151768e-06,
3.48478461e-05, 1.00083462e-05, 2.50208427e-06, 5.15211359e-05,
2.11578681e-05, 1.15799412e-05, 7.38719064e-06, 1.47383626e-05,
1.30672452e-05, 1.38680422e-04, 4.05926221e-06, 3.18614548e-06,
8.06577373e-06, 3.53450923e-06, 1.83996269e-06, 3.94102244e-06,
7.17982621e-05, 1.00657408e-05, 5.08293169e-06, 3.67364714e-06,
2.29014477e-05, 3.48240746e-06, 4.14753958e-06, 6.66786127e-06,
4.40955409e-06, 1.79899926e-05, 5.99519763e-06, 1.38429950e-05,
5.56027726e-06, 1.39362473e-05, 3.60847548e-06, 2.75285329e-05,
3.60187987e-05, 5.66070812e-05, 3.56594910e-06, 6.57450346e-06,
7.99081772e-06, 1.63463585e-06, 4.92812524e-06, 6.07251150e-06,
8.92737626e-06, 2.81335952e-06, 5.05776325e-06, 3.99102973e-05,
6.06472458e-05, 4.53967277e-06, 6.83162489e-06, 2.33467708e-05,
1.25393933e-06, 4.17351657e-06, 9.03822729e-06, 1.32900732e-05,
1.81316445e-05, 7.30276079e-05, 2.07007852e-05, 2.84792623e-06,
1.55005964e-05, 3.30787361e-06, 1.07024662e-05, 1.75124078e-05,
4.07410471e-06, 1.33024741e-05, 2.38178359e-06, 1.80862298e-06,
4.20298784e-05, 1.71086103e-05, 6.17743535e-06, 1.24997005e-05,
2.48173583e-05, 2.43331942e-05, 2.48161882e-06, 7.10804807e-06,
1.72095588e-05, 9.69264511e-06, 1.67861083e-06, 1.25047563e-05,
1.79347233e-04, 4.42927876e-06, 9.93967551e-05, 4.41379916e-06,
2.87049556e-06, 4.43178033e-06, 5.66310291e-06, 5.44323984e-06,
6.82001155e-06, 6.17301794e-06, 5.35650497e-06, 5.03396268e-06,
7.91478960e-05, 8.72708006e-06, 1.54357476e-05, 3.59996830e-06,
1.24019898e-05, 5.38674212e-05, 4.72885267e-06, 1.86716989e-05,
1.24353173e-05, 9.82651272e-06, 1.06963435e-05, 8.49228127e-06,
2.75154889e-05, 2.83566555e-06, 9.59763656e-06, 2.27382934e-05,
2.66566185e-05, 7.98125620e-05, 1.37143516e-05, 9.31568320e-06,
4.78204493e-06, 3.95825336e-05, 3.64411499e-05, 4.93884318e-06,
2.04911321e-05, 5.48169510e-06, 2.61031050e-06, 8.54471582e-05,
3.31612682e-06, 4.35054426e-06, 7.82337429e-06, 1.60672535e-05,
3.37838878e-06, 2.71972585e-05, 5.34267383e-06, 1.14730610e-05,
9.13533586e-06, 6.35510105e-06, 9.87699332e-06, 5.83947076e-06,
1.66968039e-05, 9.75395687e-06, 1.44088999e-05, 1.95850498e-06,
2.85293368e-06, 6.76804120e-06, 5.98553106e-06, 1.87422756e-05,
1.73995722e-05, 6.94113260e-06, 3.37834376e-06, 5.61583374e-06,
1.69900286e-05, 4.93906464e-06, 3.29881641e-06, 4.06461040e-05,
1.48785412e-05, 2.90521575e-06, 6.23324058e-06, 8.77616276e-06,
2.20589072e-06, 4.70841405e-06, 1.04324326e-05, 7.46386240e-06,
1.13089736e-05, 2.75994043e-05, 4.37329027e-06, 8.19561228e-06,
2.45051866e-04, 3.93271584e-05, 2.52187801e-05, 2.00633731e-05,
1.91215604e-05, 6.69975489e-05, 1.92905263e-05, 5.09152633e-05,
2.35884308e-05, 2.05309570e-05, 1.61497417e-06, 7.82334755e-05,
5.69036092e-06, 2.73724036e-05, 4.28269914e-06, 8.38000960e-06,
2.84026396e-06, 6.31747534e-05, 5.44477143e-06, 7.08292282e-06,
9.74176965e-06, 5.35768504e-06, 1.98139387e-05, 3.03709430e-06,
8.54568225e-06, 3.57346948e-06, 4.94908363e-06, 2.34943855e-05,
1.78074595e-06, 1.59825886e-05, 3.36432549e-06, 1.00663365e-05,
6.32118554e-06, 5.33060447e-05, 8.84173187e-06, 4.33160203e-05,
6.57560713e-06, 5.14128806e-06, 5.00917486e-06, 5.00752230e-06,
3.58475918e-05, 2.08952930e-04, 4.66551501e-05, 2.34075214e-05,
2.13851908e-05, 2.98014584e-05, 4.87016041e-05, 3.49383590e-05,
3.91068716e-05, 3.04496680e-05, 2.80150562e-05, 2.15844921e-05,
1.46351857e-04, 2.31622580e-05, 7.80160281e-06, 2.03538038e-05,
7.12425435e-06, 2.23648785e-05, 4.83019176e-05, 8.71380689e-06,
7.93075451e-05, 2.72965208e-05, 7.79602487e-06, 1.17199170e-05,
8.17126329e-06, 1.62598590e-05, 3.56876285e-06, 4.18981108e-06,
5.88112425e-06, 4.91517994e-06, 5.28618148e-05, 1.27649328e-05,
5.19994064e-05, 1.84776763e-05, 6.37496260e-06, 3.22680949e-04,
5.40923565e-06, 1.47737683e-05, 4.52478253e-06, 2.73298997e-06,
9.16030331e-06, 6.73434261e-06, 1.35692671e-05, 4.39103951e-06,
5.97866847e-06, 2.94498363e-06, 1.67658472e-05, 6.78966971e-05,
4.71447038e-06, 1.51047334e-05, 4.40034955e-06, 1.07245560e-05,
6.21281579e-05, 1.49911175e-05, 2.63767070e-05, 1.34302309e-06,
4.79672963e-06, 1.13071883e-06, 2.76882474e-05, 3.60243612e-05,
3.32539321e-06, 3.82541339e-06, 1.53966976e-05, 6.77922653e-05]],
dtype=float32)
Copy to Clipboard
[[('n02077923', 'sea_lion', 0.9683403),
('n02655020', 'puffer', 0.0045611714),
('n03935335', 'piggy_bank', 0.0021532692),
('n03724870', 'mask', 0.0018053611),
('n02444819', 'otter', 0.00088046247)]]
Looks nice! Xception pretrained model correctly predicted as “Sea Lion” with 0.968 prediction accuracy.
Let’s check the prediction accuracy on VGG19, VGG16, InceptionV3, MobileNetV3, NASNetMobile, and ResNet50.
[[('n02077923', 'sea_lion', 0.45364004),
('n02074367', 'dugong', 0.1379553),
('n01664065', 'loggerhead', 0.07919356),
('n01665541', 'leatherback_turtle', 0.0403896),
('n02808304', 'bath_towel', 0.018666666)]]
VGG19:
[[('n02077923', 'sea_lion', 0.8721129),
('n01664065', 'loggerhead', 0.048861112),
('n01665541', 'leatherback_turtle', 0.01896045),
('n02074367', 'dugong', 0.016715886),
('n02444819', 'otter', 0.006451091)]]
InceptionV3:
[[('n02077923', 'sea_lion', 0.7158504),
('n02444819', 'otter', 0.10402603),
('n01665541', 'leatherback_turtle', 0.0068784873),
('n02808440', 'bathtub', 0.0035360176),
('n01784675', 'centipede', 0.0034717675)]]
MobileNetV3:
[[('n02077923', 'sea_lion', 0.25503463),
('n03724870', 'mask', 0.12915507),
('n03935335', 'piggy_bank', 0.12580934),
('n02134084', 'ice_bear', 0.0388209),
('n01484850', 'great_white_shark', 0.033164274)]]
[[('n02077923', 'sea_lion', 0.50293046),
('n01484850', 'great_white_shark', 0.39009607),
('n02808440', 'bathtub', 0.028271727),
('n02655020', 'puffer', 0.0064766705),
('n04493381', 'tub', 0.0058223144)]]
[[('n02077923', 'sea_lion', 0.57366097),
('n04131690', 'saltshaker', 0.0431055),
('n03935335', 'piggy_bank', 0.041847255),
('n04423845', 'thimble', 0.016428046),
('n03724870', 'mask', 0.012995369)]]
In this example, we could see ALL pretrained models predicts as "Sea Lion".
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