In the world of Artificial Intelligence and advancement in technologies, many researchers and big companiesare working on autonomous vehicles and self-driving cars. So, for achieving accuracy in this technology, the vehicles should be able to interpret traffic signs and make decisions accordingly.
Therefore, in this project we aim to build a deep neural network model that can classify traffic signs present in the image into different categories.
Given a various traffic signs along with images dataset , we are challenged to classify traffic signs present in the image into different categories.
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For this project, we are using the public dataset available at Kaggle: Traffic Signs Dataset
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The dataset contains around 50,000 images of different traffic signs. It is further classified into 43 different classes.
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The dataset is quite varying, some of the classes have many images while some classes have few images.
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The size of the dataset is 44 MB.
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The dataset has a train folder which contains images inside each class and a test folder which we will use for testing our model.
- Software Platform:
- Jupyter Notebook
- Programming Language:
- Python
- Python Libraries:
- Modeling libraries:
- Sci-Kit Learn
- Tensorflow (keras)
- Data manipulation libraries:
- Pandas
- Numpy
- Visualization libraries
- Matplotlib
- Seaborn
- Storage libraries
- Pickle
- SQLAlchemy
- Modeling libraries: