Vehicle type recognizition - Machine Learning

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Hello, Your need to identify both type and model of a car? One approach can be first identify type and then classify model or do the both in the same program. I think neural network will work best for you. Will a simple command line application will work for classifying or you need a web interface? Regards, Samiran

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I want to discuss this project with you further, let me know the best suitable time for you to schedule the meeting, Feel free to message me at any time, i used to be online 14 hrs in a day on this website so probably you will get a quick response from my end.

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I can do this vehicle type recognition and number plate recognition software for you . In the bid amount , I can provide the website version , android application , ios application with one year support . I am available on all days to discuss and shall provide 2 work updates per week ( Tuesday and Friday ). You can release the payment as per milestones and after the milestone work is complete.

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I'd like to propose to use CNN from Tensorflow Google's OpenAI code and use it to distinguish between the vehicles. But, there are some limitations and we need to discuss them here or over chat as you prefer. First, DATA. How is your data? IS it labelled? Do you have the vehicles under different folders for different types? Are they centralized for each of the pics that's they are the significant amount of the picture in area and occupy mostly the center of the image. This makes CNN very useful, actually. Platform: The platform would be Python and the data can be just images residing on hard disk. The Tensorflow package is already build from source available with me in Ubuntu 14. There must be around 1000 pics for each of the categories of vehicles because even to build a generic classifier that tells all vehicles apart from other objects, you need 10,000s of images. Further, it depends on the robustness you want to acquire. We shall finalize the BID after discussion. I think, I'll stop here and let you know what you want to do? Message me over chat if you would like to discuss more. Thanks!

We can detect every by using computer vision algorithm by comparing pictures which we have in our system

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Any technology Matters or you want to use a specific product?

Hello! Microsoft certified data scientist experienced with Azure Machine Learning, TensorFlow, Python and R wants to give some useful insights for your project. Regards, Anton

With my past experience of working at classification problem similar to this one and my past experience with developing deep learning models,I can fully justify this project.I have worked in past with various libraries and my experience will come handy for this project.

I have more than 15 years experience in the field of analytics and data science within the financial services industry. I have developed and implemented multiple analytical solutions for organisations. Give me your problem and I will solve it.

Hi I have good experience on doing this project. I have a plan of implementing the Convolutional Neural Network on your given data set and make the Model predict the type and model of any vehicle. After the training and testing of Model, I can able to integrate it with Web or Mobile platform as your wish. I have high configured GPU machines, in which my model process your dataset with more accuracy. Please feel free to contact me anytime regarding this project. Am Happy to help you!!!! :) Regards SmartGlossa

Hi After going through this project requirement, my understanding are as follows: - Classification of Vehicles based on their features. - Predicting the new of vehicle based on the previous set - Clustering the vehicle type in their particular group. This project would involve training the data set using Neural Network or Support Vector Machine. The clustering can be done using the unsupervised learning process. My expertise include: -Machine Learning -Python - R - Database I would be able to finish this work within 15 days. My bid would be 1,50,000 INR as I have done this work earlier.

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