The purpose of this project is to develop a deep learning classification algorithm which is robust to different lighting conditions and orientations in an image unlike the previous object classifiers.
Image Classification using deep learning network
The purpose of this project is to develop a deep learning classification algorithm which is robust to different lighting conditions and orientations in an image unlike the previous object classifiers.
The following tasks will be performed in this project:
(1) It will take images from the real world
(2) Process the images
(3) Make predictions about the category of object in an image
(4) Provide results in percentage form of top prediction
This project is multi-task project which involves the underlying steps:
(a) First of all the algorithm will be trained on the desired dataset
(b) Then the optimal weights of neurons in the network i.e optimal accuracy will be obtained
(c) After error reduction and optimization it will be validated on unseen images from the real world to check the performance
(d) Then the model will be deployed on the mobile platform
The software that will be used is Keras with Tensor flow backend. For mobile deployment Android SDK will be used
This project will help in automation of survillance of areas instead of defining features of detecable objects this algorithm will train on desired dataset and then will predict the object in an image. This project will be another step towards autonomous future.
The expected deliverables of this project are listed below:
(a) Develop a deep learning algorithm
(b) Training of deep learning algorithm
(c) Error reduction and optimization
(d) Validation of unknown images
(e) Deployment on mobile platform
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| MSI GeForce GTX 1070 Ti 8GB | Equipment | 1 | 70000 | 70000 |
| MWE Gold 650 Full Modular Power Supply | Miscellaneous | 1 | 10000 | 10000 |
| Total in (Rs) | 80000 |
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