Classification of Artifacts using Machine Learning

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  Accuracy file

Inspiration

Since classification of artifacts is usually performed by visual inspection, techniques from computer vision and machine learning can be applied in order to develop such tools.

What it does

The Project titled classification of artifacts based on machine learning describes the classification of historical artifacts based on machine learning algorithms.

How we built it

Deployed a model on flask that is trained on CNN using transfer learning approach to classify artifacts into three categories namely Basket, Coin, Figure. VGG19 is used for transfer learning approach with custom output layers. such techniques can help archaeologist in their assessment and classification of archaeological finds.

Challenges we ran into

Very tricky to find dataset and then feed-it to the model

Accomplishments that we're proud of

Completed the project with 80 to 90% accuracy

What we learned

How to tackle a problem with to much pressure

What's next for Classification of Artifacts using Machine Learning

Want to make image regeneration with the help of machine learning

Built With

• flask
• keras
• numpy
• pickle
• python