Neural System Module

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1

There is an experiment that request to build Neural system Module that can identify certain pattern among other pattern in the input image.

For example: the image has the following components:
1- Pencil
2- Eraser
3- sharpener
4- Inkwell
Now, I want to build Neural system Module to identify, if the image has a pencil or not
and where it is located in the image; such as, is the pencil at the center or upper corner, …etc.

I am not sure, how to start on this problem and how to build the module

Any suggestion, or direction will be appreciated
Thanks,
Saleh

3

Building a neural system module to identify certain patterns in an image and locate them within the image is a common task in computer vision and can be approached using a deep learning method called convolutional neural networks (CNNs).

To start on this problem, you can first collect a dataset of images that contain pencils and images that do not contain pencils. It’s important to have a diverse set of images, including images with pencils in different locations and orientations. Then you can use this dataset to train a CNN.

You can use pre-trained models such as VGG, Resnet, Inception etc as a base model, and then fine-tune the model on your dataset. This can be done by using transfer learning approach, where you’ll be using the weights of pre-trained models and fine-tune them on your dataset.

Then, you can use this trained model to classify new images as containing a pencil or not, and use the output of the model to locate the pencil in the image. This can be done by using techniques such as bounding box regression or object detection algorithms such as YOLO, SSD etc.

It’s also important to note that training a neural network requires a lot of computational resources and time. You may consider using cloud-based services such as AWS or GCP to train your models.

Additionally, you can also consider using pre-built libraries such as Tensorflow, Pytorch, or OpenCV which have built-in functionality for image classification and object detection which can make your task easier.