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Merge pull request #708 from Varunshiyam/Fixes-#704-Coffee
Fixes #704 coffee Bean Classification
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# Coffee Bean Classification with Deep Learning | ||
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This project leverages deep learning to classify coffee bean types based on visual features. By training a convolutional neural network (CNN) model on labeled coffee bean images, we aim to create a system capable of identifying different types of coffee beans automatically. | ||
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## Table of Contents | ||
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- [Introduction](#introduction) | ||
- [Project Structure](#project-structure) | ||
- [Dataset](#dataset) | ||
- [Model](#model) | ||
- [Results](#results) | ||
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## Introduction | ||
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This project focuses on image classification using deep learning techniques to differentiate between various coffee bean types. This is particularly useful in industries that require high-quality control and consistent coffee bean quality. | ||
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## Project Structure | ||
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```plaintext | ||
├── data/ # Dataset folder containing images | ||
├── notebooks/ # Jupyter notebooks for experimentation | ||
├── src/ # Source files for model training and evaluation | ||
├── models/ # Saved models and checkpoints | ||
├── results/ # Evaluation results and metrics | ||
└── README.md # Project documentation | ||
``` | ||
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## Dataset | ||
The dataset contains images of coffee beans from different types. These images are preprocessed for uniformity, and are then split into training, validation, and testing sets. | ||
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## Model | ||
We implemented a convolutional neural network (CNN) model for image classification. Key layers include: | ||
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- Convolutional layers for feature extraction | ||
- Pooling layers to reduce dimensionality | ||
- Dense layers to classify images based on extracted features | ||
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## Results | ||
The model's performance is evaluated using metrics such as accuracy, precision, recall, and F1-score on the test dataset. The model achieves satisfactory results in distinguishing between coffee bean types. | ||
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