Project Overview
Playing Card Classifier is a deep learning and computer vision project I developed to recognize playing cards from images using PyTorch and EfficientNet-B0.
The model was trained to classify 53 playing card classes and achieved approximately 92% validation accuracy during development.
I also built a web application using Streamlit, allowing users to upload a playing card image and receive predictions from the trained model.
The application displays the predicted card along with the top five predictions and their probabilities.
This project was part of my early learning journey in Machine Learning, where I explored how to train an image classification model and use it in an interactive application.
The Problem
Recognizing playing cards is easy for humans, but a computer needs to learn how to identify the differences between cards.
Each card has its own visual characteristics, including:
- Card rank and suit.
- Symbols and numbers.
- Colors and patterns.
- Different orientations and image backgrounds.
The challenge was to train a model capable of identifying the correct card based on its visual features.
I wanted to explore how Convolutional Neural Networks (CNNs) and Transfer Learning could solve this image classification problem.
What I Built
I developed a playing card classification system consisting of two main components.
1. Deep Learning Model
A PyTorch-based image classification model using the EfficientNet-B0 architecture.
The model was trained to recognize 53 playing card classes.
2. Streamlit Web Application
An interactive web interface that allows users to upload card images and see the model's predictions.
The application includes:
- Image upload functionality.
- Playing card classification.
- Predicted card identification.
- Top-5 predictions.
- Prediction probabilities.
This allowed me to move beyond training a model in a notebook and demonstrate it through a usable application.
How the Model Works
The project follows two main stages: Training and Inference.
Stage 1 — Training the Model
I used Google Colab as my model development and training environment.
The model was developed using PyTorch and the EfficientNet-B0 architecture.
EfficientNet-B0 is a convolutional neural network architecture designed for efficient image classification.
I explored Transfer Learning, which uses knowledge learned by a pretrained neural network as a starting point for a new classification problem.
Instead of developing an entire image recognition system from the beginning, this approach helped me work with an existing architecture for the playing card classification task.
During training, the model learned to associate image features with the supported card classes.
Stage 2 — Using the Trained Model
After training, the model could be used for inference.
Inference means using an already-trained model to make predictions on new inputs.
The general workflow is:
User uploads a card image
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Image preparation
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Trained EfficientNet-B0
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Model predictions
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Top 5 card classes
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Streamlit displays
the result
The model does not need to be retrained every time someone uploads a card.
Instead, the web application uses the trained model to generate a prediction.
Model Performance
During development, the model achieved approximately:
Validation Accuracy: 92%
This means that the model correctly classified roughly 92% of the examples in the validation evaluation reported during training.
Validation accuracy is useful for understanding model performance during development.
However, it should not automatically be interpreted as guaranteed accuracy on every real-world image.
Performance can vary depending on image quality, lighting, background, orientation, and how similar new images are to the training data.
Top-5 Predictions
One feature I added to the Streamlit application was displaying the top five predictions with probabilities.
Rather than presenting only one predicted card, the model can show several possible classes ranked by its prediction scores.
For example, the output conceptually looks like:
Uploaded Image: Playing Card
Prediction Results:
1. Most likely card
2. Second most likely card
3. Third most likely card
4. Fourth most likely card
5. Fifth most likely card
Each prediction is accompanied by its probability score.
This makes the output more informative because users can see which alternative card classes the model considers possible.
These probabilities represent model prediction scores, not guaranteed measures of correctness.
Technologies Used
Python
Used for developing the machine learning model and application.
PyTorch
The main deep learning framework used for model training and inference.
EfficientNet-B0
The CNN architecture used for playing card image classification.
Transfer Learning
An approach that helped adapt an existing neural network architecture to the playing card recognition problem.
Google Colab
The environment used to experiment with and train the model.
Streamlit
The framework used to build the interactive web application.
Computer Vision
The field of artificial intelligence that focuses on interpreting visual information such as images.
Why I Built This Project
When I started learning Machine Learning, I wanted to understand how image classification models worked beyond theoretical explanations.
This project gave me an opportunity to explore practical deep learning concepts.
I was especially interested in understanding the complete process:
- Preparing images for classification.
- Working with a CNN architecture.
- Training a model using PyTorch.
- Evaluating classification performance.
- Using a trained model for inference.
- Connecting the model to a web application.
- Presenting predictions in a way users can understand.
It helped me see how a machine learning experiment could become an interactive application.
What I Learned
This project strengthened my understanding of several important Machine Learning concepts.
Image Classification
How a model learns to recognize different categories from images.
Convolutional Neural Networks
How neural networks can extract useful visual features for classification.
Transfer Learning
How an existing pretrained architecture can support a new classification task.
Model Training and Validation
Why model performance should be evaluated using data separate from the training examples.
Inference
How a trained model can generate predictions without requiring retraining for every input.
Model Deployment
How an AI model can be connected to a simple application so users can interact with it.
The biggest lesson was understanding the difference between training a neural network and using a trained neural network in an actual application.
Project Links
- Source Code: View on GitHub
- Live Demo: Try the Playing Card Classifier
- LinkedIn Post: View my original project announcement
This project represents an important step in my early learning journey with PyTorch, CNNs, Transfer Learning, and practical image classification.
