# Image Classification Model using Deep Learning

## 1. Introduction
This project aims to develop an image classification model using deep learning techniques. The model is trained to classify images into predefined categories based on the features learned from the input data.

## 2. Model Architecture
The image classification model employs a convolutional neural network (CNN) architecture. CNNs are well-suited for image classification tasks as they can automatically learn hierarchical representations of images. The model architecture consists of multiple convolutional layers followed by pooling layers for feature extraction, followed by fully connected layers for classification.

## 3. How to Guide
To use the image classification model:
1. Prepare dataset: Organize images into folders based on their categories. Each folder should represent a different class.
2. Data Preprocessing: Resize images to a uniform size and perform normalization if necessary.
3. Model Training: Train the image classification model using the prepared dataset. Adjust hyperparameters such as learning rate, batch size, and number of epochs as needed.
4. Model Evaluation: Evaluate the trained model on a separate test dataset to assess its performance. Calculate metrics such as accuracy, precision, recall, and F1-score.
5. Model Deployment: Deploy the trained model for inference on new unseen images. Integrate the model into application or use it for batch processing.

## 4. License
This project is licensed under the [MIT License](https://opensource.org/licenses/MIT). You are free to use, modify, and distribute the code for both commercial and non-commercial purposes. See the `LICENSE` file for more details.

## 5. Contributor
- [Mehadi Hasan] - [Your GitHub Profile](https://github.com/NAYANCSE27)