Inspiration
The theme of Hack-ccelerate helped us discover this exciting project. We wished to explore Computer vision-based projects, and hence we chose to create an automobile parts classifier using CNN.
What it does
Our deep learning model classifies 14 different types of automobile parts.
How we built it
The model was built mainly using the TensorFlow Keras API.
Challenges we ran into
The main challenge we faced was handling the data in such a way as to prevent overfitting. This was overcome using data augmentation and a proper CNN architecture, that was created with thorough experimentation.
Accomplishments that we're proud of
We were able to achieve good accuracy, without using any kind of transfer learning. Our custom model was able to compete with already established TensorFlow architectures.
What we learned
Working on creating a custom CNN architecture helped us in creating a strong intuition about how different architectures perform, how different layers affect training, etc.
What's next for Automobile Parts Classifier
The next goal is to improve the generalisation of the model.
Built With
- matplotlib
- python
- tensorflow
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