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README.md

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@@ -61,8 +61,9 @@ Sample classification results
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### Evaluation
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Accuracy of the network on the 550 test images: 99.09%
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Confusion Matrix
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----------------
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**Confusion Matrix**
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```
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[[50 0 0 0 0 0 0 0 0 0 0]
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[ 0 50 0 0 0 0 0 0 0 0 0]
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[ 0 0 50 0 0 0 0 0 0 0 0]
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[ 0 0 0 0 0 0 0 0 50 0 0]
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[ 0 0 0 0 0 0 0 0 0 50 0]
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[ 0 0 0 0 0 0 0 0 0 0 50]]
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Per class accuracy
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------------------
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```
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**Per class accuracy**
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```
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Accuracy of class apple : 100.00 %
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Accuracy of class atm card : 100.00 %
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Accuracy of class camera : 100.00 %
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Accuracy of class broom : 100.00 %
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Accuracy of class bulb : 100.00 %
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Accuracy of class calender : 100.00 %
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```
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### Observations
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1. In **transfer learning**, if your custom dataset is **similar** to the pretrained model's training dataset, then you can easily acheive very **high accuracy**(>90) with very **few training epochs**(<10).

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