### python-2.7

#### vehicle type identification with neural network

```I was given a project on vehicle type identification with neural network and that is how I came to know the awesomeness of neural technology.
I am a beginner with this field, but I have sufficient materials to learn it. I just want to know some good places to start for this project specifically, as my biggest problem is that I don't have very much time. I would really appreciate any help. Most importantly, I want to learn how to match patterns with images (in my case, vehicles).
I'd also like to know if python is a good language to start this in, as I'm most comfortable with it.
I am having some images of cars as input and I need to classify those cars by there model number.
Eg: Audi A4,Audi A6,Audi A8,etc
```
```You didn't say whether you can use an existing framework or need to implement the solution from scratch, but either way Python is excellent language for coding neural networks.
If you can use a framework, check out Theano, which is written in Python and is the most complete neural network framework available in any language:
http://www.deeplearning.net/software/theano/
If you need to write your implementation from scratch, look at the book 'Machine Learning, An Algorithmic Perspective' by Stephen Marsland. It contains example Python code for implementing a basic multilayered neural network.
As for how to proceed, you'll want to convert your images into 1-D input vectors. Don't worry about losing the 2-D information, the network will learn 'receptive fields' on its own that extract 2-D features. Normalize the pixel intensities to a -1 to 1 range (or better yet, 0 mean with a standard deviation of 1). If the images are already centered and normalized to roughly the same size than a simple feed-forward network should be sufficient. If the cars vary wildly in angle or distance from the camera, you may need to use a convolutional neural network, but that's much more complex to implement (there are examples in the Theano documentation). For a basic feed-forward network try using two hidden layers and anywhere from 0.5 to 1.5 x the number of pixels in each layer.
Break your dataset into separate training, validation, and testing sets (perhaps with a 0.6, 0.2, 0.2 ratio respectively) and make sure each image only appears in one set. Train ONLY on the training set, and don't use any regularization until you're getting close to 100% of the training instances correct. You can use the validation set to monitor progress on instances that you're not training on. Performance should be worse on the validation set than the training set. Stop training when the performance on the validation set stops improving. Once you've accomplished this you can try different regularization constants and choose the one that results in the best validation set performance. The test set will tell you how well your final result is performing (but don't change anything based on test set results, or you risk overfitting to that too!).
If your car images are very complex and varied and you cannot get a basic feed-forward net to perform well, you might consider using 'deep learning'. That is, add more layers and pre-train them using unsupervised training. There's a detailed tutorial on how to do this here (though all the code examples are in MatLab/Octave):
http://ufldl.stanford.edu/wiki/index.php/UFLDL_Tutorial
Again, that adds a lot of complexity. Try it with a basic feed-forward NN first.```

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