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---
title: Arabic2English
colorFrom: blue
colorTo: purple
sdk: gradio
license: mit
language:
- en
- ar
pipeline_tag: translation
metrics:
- accuracy
library_name: transformers
---
# Model Card
## Overview
- **Model name:** Arabic2English Translation
- **Model description:** Translates between Arabic and English.
- **Authors:** Alif Al Hasan
- **Repository link:** https://huggingface.co/spaces/alifalhasan/arabic2english/tree/main
- **License:** MIT
- **Contact information:** [email protected]
---
# Arabic2English Translation
A simple and well designed web app to translate between **Arabic** and **English**.
### Requirements
- gradio
- torch>=1.6
- torchtext==0.6
- transformers
- nltk
- pandas
- spacy
- https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.7.1/en_core_web_sm-3.7.1-py3-none-any.whl
### Table Of Contents
- [Introduction](#introduction)
- [Model Architecture](#model-architecture)
- [Project Architecture](#project-architecture)
- [How To Run](#how-to-run)
- [License](#license)
- [Contributor](#contributor)
### Introduction
A simple and well designed web app to identify the emblem of the top 5 teams of **EPL**. This model has been trained with a balanced dataset which contains almost **5k** images of the emblems of the teams.
### Model Architecture
The model utilizes a straightforward convolutional neural network (CNN) architecture, comprising the following layers:
1. **Convolutional Layer:**
- 32 filters, each of size 3x3
- ReLU activation function
- Input shape: 224x224x3 (RGB images)
- Extracts spatial features from input images.
2. **Max Pooling Layer:**
- Pool size: 2x2
- Reduces spatial dimensions for capturing more global features.
3. **Flattening Layer:**
- Flattens the 2D feature maps into a 1D vector for input to dense layers.
4. **Dense Layer 1:**
- 64 neurons
- ReLU activation function
5. **Output Layer (Dense Layer 2):**
- 5 neurons (matching the number of classes)
- Softmax activation to produce probability scores for each class.
**Key Points:**
- Input image size: 224x224 pixels
- Optimizer: Adam with a learning rate of 0.001
- Loss function: Categorical crossentropy
- Performance metric: Accuracy
**Visual Representation:**
[Input image (224x224x3)] --> [Conv2D] --> [MaxPooling2D] --> [Flatten] --> [Dense 1] --> [Output Layer (Dense 2)] --> [Predicted class]
### Prject Architecture
```
├── data
│ └── arsenal - images of arsenal's emblem.
│ └── chelsea - images of chelsea's emblem.
│ └── liverpool - images of liverpool's emblem.
│ └── manchester-city - images of manchester-city's emblem.
│ └── manchester-united - images of united's emblem.
│
│
├── model
│ └── football_logo_model.h5 - generated model.
│
│
├── src
│ └── classify
│ └── classify.py - this module classifies the emblem from input image.
│ └── train
│ └── trainer.py - this module trains the model.
│
│
├── app.py - this module starts the app interface.
│
│
├── LICENSE - license file of this project.
│
│
├── README.md - readme file of this project.
│
│
├── requirements.txt - list of required packages.
```
### How To Run
First, install dependencies
```bash
# clone project
git clone https://huggingface.co/spaces/alifalhasan/epl-top5-emblem-classifier
# install project
cd epl-top5-emblem-classifier
pip install -r requirements.txt
```
Next, download the dataset from [here](https://drive.google.com/file/d/1O5Mm-86AlUf5fUYf1NS8J_t22h7h_UbQ/view?usp=sharing). First unzip the folder. **dataset** folder contains **five** more folders. Copy them and paste into the **data** directory of this project folder.
Now train the model using this command:
```bash
python src/train/trainer.py
```
Finally, deploy the model using this command:
```bash
python app.py
```
### License
Distributed under the MIT License. See `LICENSE` for more information.
### Contributor
Alif Al Hasan - [@alifalhasan](https://huggingface.co/alifalhasan) - [email protected]
Project Link: [https://huggingface.co/spaces/alifalhasan/epl-top5-emblem-classifier](https://huggingface.co/spaces/alifalhasan/epl-top5-emblem-classifier) |