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	---
	title: Deep Learning 1
	emoji: 📉
	colorFrom: blue
	colorTo: purple
	sdk: gradio
	sdk_version: 4.12.0
	app_file: app.py
	pinned: false
	license: mit
	language:
	- en
	tags:
	- sports
	- soccer
	- epl
	- arsenal
	- chelsea
	- liverpool
	- man city
	- man united
	metrics:
	- accuracy
	library_name: gradio
	pipeline_tag: image-to-text
	---

	# Model Card

	## Overview

	- Model name: Top 5 EPL Teams' Emblem Identifier
	- Model description: Identifies the emblem of the top 5 English Premier League (EPL) teams from images using a convolutional neural network (CNN).
	- Authors: Alif Al Hasan
	- Repository link: https://huggingface.co/spaces/alifalhasan/deep-learning-1/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 Endpoint: Try the model live and interact with it directly here: [https://huggingface.co/spaces/alifalhasan/deep-learning-1](https://huggingface.co/spaces/alifalhasan/deep-learning-1)
	- 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/deep-learning-1

	# install project
	cd deep-learning-1
	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/deep-learning-1](https://huggingface.co/spaces/alifalhasan/deep-learning-1)