The input dataset is a combination of various datasets that we will be using for the problem statement. Preprocessing the dataset includes Normalisation and Augmentation.

The dataset is then split into three parts mainly Training(80%), Testing(10%) and Validation(10%) datasets.

The model then classifies, detects and segments the object accordingly.

1. AI Analytics toolkit:

• Extension for Tensorflow
• Intel Neural Compressor

2. CPU and GPU compiling:

• SYCL
• DPC++

3. Frameworks:

• Opencv
• Tensorflow
• scikit-learn

4. Languages:

• Python
• C++

This Section must contain set of instructions required to clone and run the prototype, so that it can be tested and deeply analysed

• we had have the opportunity to explore a range of Intel's software development tools and libraries, including the AI analytics toolkit and the SYCL/DPC++ libraries.
• The AI analytics toolkit is a set of libraries and tools that can be used for machine learning and data analytics.
• The SYCL/DPC++ libraries, on the other hand, are a set of C++ libraries that can be used to write parallel code for a range of hardware platforms. These libraries enable developers to write code that can run efficiently on CPUs, GPUs, and other accelerators, making it possible to take advantage of the full power of modern hardware.
• By leveraging the AI analytics toolkit and SYCL/DPC++ libraries, developers can write code that is optimized for the hardware platform they are targeting, and that can run efficiently on a range of devices. This can help to speed up development time, improve performance, and enable developers to tackle more complex problems.