JunkWaxHero 🦸‍♂️

JunkWaxHero is your go-to computer vision model for identifying "Junk Wax" baseball card sets from the golden era of 1980 to 1999. Whether you're a collector, dealer, or enthusiast, JunkWaxHero simplifies the process of cataloging and verifying your collection with high precision and recall. The purpose of JunkWaxHero is to provide a model that quickly identifies baseball card sets without relying on SaaS-based inference APIs.

📦 Available Formats

JunkWaxHero is available in ONNX format to cater to different deployment needs and preferences.

ONNX: Perfect for interoperability across various platforms and optimizing for different hardware accelerators.

📊 Training Details

Training Completion Date: January 10, 2025, at 1:32:46 PM
Training Platform: Azure Custom Vision
Domain: General (Compact) [S1]

Overall Performance Metrics

Metric	Value
Precision	98.8%
Recall	98.8%
mAP	99.4%

📁 Dataset

The training dataset for JunkWaxHero was meticulously curated to ensure robustness and versatility. It includes baseball card images captured on multiple different surfaces and backgrounds, with varying zoom levels to simulate real-world conditions. All images adhere to a 4:3 image ratio to align with modern cell phone portrait modes. The baseball cards in the dataset are vertically aligned to maintain consistency during training. Support for horizontally aligned cards photographed in their horizontal orientation is planned for future iterations to enhance the model's flexibility.

🎯 Performance Per Set

JunkWaxHero has been meticulously trained to recognize the following baseball card sets with exceptional accuracy:

Year	Set	Included	Precision	Recall	A.P.
1984	Topps	✅	100.0%	100.0%	100.0%
1987	Topps	✅	95.8%	100.0%	100.0%
1989	Upper Deck	✅	100.0%	92.3%	100.0%
1989	Fleer	✅	100.0%	100.0%	100.0%
1989	Bowman	✅	100.0%	94.7%	94.7%
1990	Topps	✅	100.0%	100.0%	100.0%
1990	Donruss	✅	100.0%	100.0%	100.0%
1991	Upper Deck	✅	100.0%	100.0%	100.0%
1993	Topps	✅	94.7%	100.0%	100.0%

Included sets are marked with a ✅, while excluded sets (if any) would be marked with a ❌.

🚀 Getting Started

Repository

Access the repository on Hugging Face: https://huggingface.co/enusbaum/JunkWaxHero/

Installation

To integrate JunkWaxHero into your projects, choose the format that best fits your workflow:

ONNX

Install ONNX Runtime:
```
pip install onnxruntime
```
Download the ONNX Model:

Navigate to the ONNX directory in the repository and download the model.onnx file from https://huggingface.co/enusbaum/JunkWaxHero/tree/main/onnx.

Load the ONNX Model:

import onnxruntime as ort
import numpy as np

# Path to the downloaded ONNX model
MODEL_PATH = 'path/to/junkwaxhero/onnx/junkwaxhero.onnx'

# Initialize the ONNX runtime session
session = ort.InferenceSession(MODEL_PATH)

# Get input and output names
input_name = session.get_inputs()[0].name
output_name = session.get_outputs()[0].name

# Example function to run inference
def predict(image):
    image = np.expand_dims(image, axis=0).astype(np.float32)  # Add batch dimension and ensure correct type
    predictions = session.run([output_name], {input_name: image})
    return predictions

Usage

Prepare Your Images: Ensure your baseball card images are clear and well-lit for optimal identification. Images should be in a 4:3 ratio and vertically aligned.
Load the Model: Use the appropriate code snippet above based on your chosen format (TensorFlow or ONNX).

Identify Sets: Pass your images through JunkWaxHero to accurately identify the set they belong to.

from PIL import Image

# Load and preprocess the image
def load_image(image_path):
    image = Image.open(image_path)
    image = image.resize((width, height))  # Resize to the input size expected by the model
    image = np.array(image)
    return image

# Example usage
image = load_image('path/to/baseball_card.jpg')
predictions = predict(image)

# Get the predicted label
predicted_label = labels[np.argmax(predictions)]
print(f'Predicted Set: {predicted_label}')

Note: Replace (width, height) with the actual dimensions expected by your model.

Optimization

Leverage the high precision and recall rates to maintain an organized and verified collection. Depending on your deployment environment, choose between TensorFlow for seamless integration with TensorFlow ecosystems or ONNX for broader platform compatibility and performance optimizations.

🔍 Use Cases

Collectors: Quickly verify and catalog your baseball card collection.
Dealers: Streamline inventory management with accurate set identification.
Enthusiasts: Explore and discover sets with ease using advanced computer vision techniques.

🛠️ Technical Details

JunkWaxHero leverages state-of-the-art computer vision architectures optimized for identifying baseball card sets from images. Trained on a diverse dataset spanning multiple decades, the model ensures robustness and reliability across various card conditions and designs. By offering both TensorFlow and ONNX formats, JunkWaxHero provides flexibility for different deployment scenarios, whether you're integrating into existing TensorFlow pipelines or seeking cross-platform compatibility with ONNX.

📄 License

This project is licensed under the MIT License.

📫 Contact

For questions, suggestions, or support, please reach out to [email protected].

Happy Collecting!

enusbaum
/

JunkWaxHero