Paper PublishingImage Classification And Inference Engine For Machine Learning
Objective
Our goal is to detect and recognize animals like Tigers, Cats, Dogs and Birds. In this application we will implement transfer learning based deep learning models like ResNet50, VGG16 and a custom DNN model. At the same time we will compare these models performance.
Abstract
The main issue in image classification is features extraction and image vector representation. We expose the Bag of Features method used to find image representation. Class prediction accuracy of varying classifiers algorithms is measured on Caltech 101 images.
For feature extraction functions we evaluate the use of the classical Speed up Robust Features technique against global colour feature extraction. The purpose of our work is to guess the best machine learning framework techniques to recognize the stop sign images. The trained model will be integrated into a robotic system in a future work.
Keywords: Animal Recognition, Computer Vision, Neural Networks, CNN, Transfer Learning, Pre-Trained Models.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
HARDWARE SPECIFICATIONS:
- Processor: I3/Intel
- Processor RAM: 4GB (min)
- Hard Disk: 128 GB
- Key Board: Standard Windows Keyboard
- Mouse: Two or Three Button Mouse
- Monitor: Any
SOFTWARE SPECIFICATIONS:
- Operating System: Windows 7+
- Server-side Script: Python 3.6+
- IDE: PyCharm
- Libraries Used: Pandas, Numpy, sklearn, TensorFlow.
Learning Outcomes
- Scope of Real Time Application Scenarios.
- What is a search engine and how browser can work.
- What type of technology versions?
- Need of PyCharm-IDE to develop a web application.
- How to implement segmentation.
- Where this application can be used.
- What are the diseases attacked by the fruits.
- Features of OpenCV.
- About transfer learning.
- How to create a models.
- Working Procedure.
- Testing Techniques.
- How to run and deploy the applications.
- Introduction to basic technologies.
- How application works.
- Input and Output modules.
- How test the application based on user inputs and observe the output.
- Project Development Skills:
- Problem analyzing skills.
- Problem solving skills.
- Creativity and imaginary skills.
- Programming skills.
- Deployment.
- Testing skills.
- Debugging skills.
- Project presentation skills.
- Thesis writing skills.
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Disclaimer - Takeoff Edu Group Projects, are not associated or affiliated with IEEE, in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, and not projects of or by IEEE.
