# sifat192/rice-type-classification-pytorch architecture

> Architecture diagram of the GitHub repository sifat192/rice-type-classification-pytorch, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-09-30). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/sifat192/rice-type-classification-pytorch
- Repository: https://github.com/sifat192/rice-type-classification-pytorch

## Overview

This repository is a PyTorch rice-type classifier. Its documented workflow takes morphological rice measurements through preprocessing and a train/test split, then uses a custom Dataset and DataLoader to train a neural network, evaluate it on test data, and predict rice type. The tree contains a notebook but no sampled notebook source, so the graph relies on the README’s workflow and capability descriptions; implementation-level calls and any additional integrations cannot be verified.

Main entry points: [`Rice_classification.ipynb`](https://github.com/sifat192/rice-type-classification-pytorch/blob/main/Rice_classification.ipynb) (Feature Preprocessing)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_data_prep["Data Preparation"]
  node_preprocessing["Feature Preprocessing"]
  node_split["Train/Test Split"]
  node_dataset["Custom Dataset"]
  node_loader["DataLoader"]
end

subgraph group_modeling["Modeling"]
  node_network["Neural Network"]
  node_training["Model Training"]
end

subgraph group_results["Evaluation and Prediction"]
  node_evaluation["Test Evaluation"]
  node_prediction["Rice Prediction"]
end

node_rice_data["Rice Dataset"]

node_rice_data -->|"provides features"| node_preprocessing
node_preprocessing -->|"prepares data"| node_split
node_split -->|"partitions data"| node_dataset
node_dataset -->|"supplies samples"| node_loader
node_loader -->|"feeds batches"| node_training
node_training -->|"optimizes"| node_network
node_network -->|"evaluated on test set"| node_evaluation
node_evaluation -->|"supports prediction"| node_prediction

click node_preprocessing "https://github.com/sifat192/rice-type-classification-pytorch/blob/main/Rice_classification.ipynb"
click node_split "https://github.com/sifat192/rice-type-classification-pytorch/blob/main/Rice_classification.ipynb"
click node_dataset "https://github.com/sifat192/rice-type-classification-pytorch/blob/main/Rice_classification.ipynb"
click node_loader "https://github.com/sifat192/rice-type-classification-pytorch/blob/main/Rice_classification.ipynb"
click node_network "https://github.com/sifat192/rice-type-classification-pytorch/blob/main/Rice_classification.ipynb"
click node_training "https://github.com/sifat192/rice-type-classification-pytorch/blob/main/Rice_classification.ipynb"
click node_evaluation "https://github.com/sifat192/rice-type-classification-pytorch/blob/main/Rice_classification.ipynb"
click node_prediction "https://github.com/sifat192/rice-type-classification-pytorch/blob/main/Rice_classification.ipynb"

classDef toneNeutral fill:#f8fafc,stroke:#334155,stroke-width:1.5px,color:#0f172a
classDef toneBlue fill:#dbeafe,stroke:#2563eb,stroke-width:1.5px,color:#172554
classDef toneAmber fill:#fef3c7,stroke:#d97706,stroke-width:1.5px,color:#78350f
classDef toneMint fill:#dcfce7,stroke:#16a34a,stroke-width:1.5px,color:#14532d
classDef toneRose fill:#ffe4e6,stroke:#e11d48,stroke-width:1.5px,color:#881337
classDef toneIndigo fill:#e0e7ff,stroke:#4f46e5,stroke-width:1.5px,color:#312e81
classDef toneTeal fill:#ccfbf1,stroke:#0f766e,stroke-width:1.5px,color:#134e4a
class node_preprocessing,node_split,node_dataset,node_loader toneBlue
class node_network,node_training toneAmber
class node_evaluation,node_prediction toneMint
class node_rice_data toneIndigo
```

## Components

### Data Preparation

- [Feature Preprocessing](https://github.com/sifat192/rice-type-classification-pytorch/blob/main/Rice_classification.ipynb): `Rice_classification.ipynb`
- [Train/Test Split](https://github.com/sifat192/rice-type-classification-pytorch/blob/main/Rice_classification.ipynb): `Rice_classification.ipynb`
- [Custom Dataset](https://github.com/sifat192/rice-type-classification-pytorch/blob/main/Rice_classification.ipynb): `Rice_classification.ipynb`
- [DataLoader](https://github.com/sifat192/rice-type-classification-pytorch/blob/main/Rice_classification.ipynb): `Rice_classification.ipynb`

### Modeling

- [Neural Network](https://github.com/sifat192/rice-type-classification-pytorch/blob/main/Rice_classification.ipynb): `Rice_classification.ipynb`
- [Model Training](https://github.com/sifat192/rice-type-classification-pytorch/blob/main/Rice_classification.ipynb): `Rice_classification.ipynb`

### Evaluation and Prediction

- [Test Evaluation](https://github.com/sifat192/rice-type-classification-pytorch/blob/main/Rice_classification.ipynb): `Rice_classification.ipynb`
- [Rice Prediction](https://github.com/sifat192/rice-type-classification-pytorch/blob/main/Rice_classification.ipynb): `Rice_classification.ipynb`

### Other components

- **Rice Dataset**

## Connections

- Rice Dataset → Feature Preprocessing: provides features (evidence: `README.md`)
- Feature Preprocessing → Train/Test Split: prepares data (evidence: `README.md`)
- Train/Test Split → Custom Dataset: partitions data (evidence: `README.md`)
- Custom Dataset → DataLoader: supplies samples (evidence: `README.md`)
- DataLoader → Model Training: feeds batches (evidence: `README.md`)
- Model Training → Neural Network: optimizes (evidence: `README.md`)
- Neural Network → Test Evaluation: evaluated on test set (evidence: `README.md`)
- Test Evaluation → Rice Prediction: supports prediction (evidence: `README.md`)