---
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:** alif.stu2017@juniv.edu


---


# 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) - alif.stu2017@juniv.edu

Project Link: [https://huggingface.co/spaces/alifalhasan/epl-top5-emblem-classifier](https://huggingface.co/spaces/alifalhasan/epl-top5-emblem-classifier)