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README.md

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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]
+
+## Performance
+
+- **Metrics:** Accuracy (100%)
+- **Dataset:** Approximately 5,000 images of EPL team emblems, balanced across classes. Sources and preprocessing steps described in detail further below.
+
+## Data
+
+- **Training data:**
+    - Size: 5,000 images
+    - Class distribution: Balanced (1,000 images per class)
+    - Sources: [English Premier League Logo Detection](https://www.kaggle.com/datasets/alexteboul/english-premier-league-logo-detection-20k-images)
+    - Preprocessing: Resizing to 224x224 pixels, normalization
+- **Potential biases:** Currently unknown biases in the dataset.
+
+## Inference
+
+- **Input:**
+    - Format: JPEG or PNG images
+    - Size: 224x224 pixels
+    - Color space: RGB
+- **Output:** Predicted class probabilities for each of the 5 EPL teams.
+- **Inference API:** [https://huggingface.co/spaces/alifalhasan/epl-top5-emblem-classifier](https://huggingface.co/spaces/alifalhasan/epl-top5-emblem-classifier)
+- **Usage instructions:** Simply upload an image of any of the EPL's top 5 team's emblem to get a prediction.
+
+## Ethics
+
+- **Potential biases:** The model may inherit biases from the training data, such as over- or under-representation of certain team emblems.
+- **Mitigation strategies:** Further exploration of dataset biases and potential augmentation techniques to address them.
+- **Ethical considerations:** 
+    - Awareness of potential biases and limitations in the model's predictions.
+    - Responsible use of the model, avoiding harmful generalizations or discrimination.
+    - Respect for the rights and privacy of individuals and organizations associated with the EPL teams.
+
+---
+
+
+# Top 5 EPL Teams' Emblem Identifier
+A simple and well designed web app to identify the emblem of the top 5 teams of **EPL(English Premier League)** namely **Arsenal, Chelsea, Liverpool, Manchester City** and **Manchester United**.
+
+
+### Requirements
+- [Python 3.11](https://python.org/)
+- [NumPy](https://numpy.org/)
+- [SciPy](https://scipy.org/)
+- [Gradio](https://www.gradio.app/)
+- [Tensorflow](https://tensorflow.org/)
+
+
+### 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)