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It is a AI Capstone project done as a part of IBM AI Engineering

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AI-Capstone-Project

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Dataset used:-

https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DL0321EN/data/concrete_data_week4.zip

Course:-

https://www.coursera.org/learn/ai-deep-learning-capstone

About the repository:-

1 . This Repository contains the 4 week AI Capstone project assignment using keras as a part of IBM-AI Engineering in which we need to classify a image containing a stone cracked or not by processing 40000 images in which nearly 30000 for training and 10000 for validation.
2 . 4th week is final assignment in which we need to campare performance in between pretrained models like Resnet50 and VGG16 using keras.

Performance of Models:-

I ran both models to 2 epochs. Performance is as shown in below table.

Model Training Accuracy Validation Accuracy Test Accuracy Valdation Loss Training Loss
Resnet 50 91.08% 93.88% 54.19 0.0016 0.0070
VGG16 99.76% 99.81% 98.0% 7.4741e-05 0.0074

Conclusion:-

In the 2 models by the above data we can conclude VGG16 is appropriate for the above dataset. After all my final assignment is passed and graded 100%.
You can check it out here

Licence:-

MIT Licence

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It is a AI Capstone project done as a part of IBM AI Engineering

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