metadata

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

Introduction
Model Architecture
Project Architecture
How To Run
License
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:

Convolutional Layer:
- 32 filters, each of size 3x3
- ReLU activation function
- Input shape: 224x224x3 (RGB images)
- Extracts spatial features from input images.
Max Pooling Layer:
- Pool size: 2x2
- Reduces spatial dimensions for capturing more global features.
Flattening Layer:
- Flattens the 2D feature maps into a 1D vector for input to dense layers.
Dense Layer 1:
- 64 neurons
- ReLU activation function
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

# 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. 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:

python src/train/trainer.py

Finally, deploy the model using this command:

python app.py

License

Distributed under the MIT License. See LICENSE for more information.

Contributor

Alif Al Hasan - @alifalhasan - [email protected]

Project Link: https://huggingface.co/spaces/alifalhasan/epl-top5-emblem-classifier

alifalhasan
/

arabic2english