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https://github.com/junkwaxdata/junkwaxdetection
A Object Recognition Machine Learning Model that identifies Junk Wax Sets of Sports Cards
https://github.com/junkwaxdata/junkwaxdetection
baseball baseball-cards computer-vision machine-learning object-detection onnx onnx-model tensorflow tensorflow-model
Last synced: 10 days ago
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A Object Recognition Machine Learning Model that identifies Junk Wax Sets of Sports Cards
- Host: GitHub
- URL: https://github.com/junkwaxdata/junkwaxdetection
- Owner: JunkWaxData
- License: mit
- Created: 2025-01-14T22:25:14.000Z (24 days ago)
- Default Branch: main
- Last Pushed: 2025-01-22T01:04:44.000Z (17 days ago)
- Last Synced: 2025-01-22T02:19:04.287Z (17 days ago)
- Topics: baseball, baseball-cards, computer-vision, machine-learning, object-detection, onnx, onnx-model, tensorflow, tensorflow-model
- Language: C#
- Homepage: https://www.junkwaxdata.com
- Size: 53.7 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Junk Wax Sports Cards Object Detection Model 🎴⚾
Welcome to the repository **JunkWaxDetection**, hosted by the GitHub organization **JunkWaxData**. This Machine Learning model is designed to identify sports cards from the overproduced "junk wax" era (1985–1996), with exceptional precision and recall metrics. Whether you're a collector, seller, or enthusiast, this model can streamline the identification of cards from various iconic sets.
## Model Overview 🧠
- **Model Version:** Iteration 26
- **Domain:** General (compact) [S1]
### Performance Metrics 📊
- **Precision:** 98.7%
- **Recall:** 98.4%
- **mAP:** 99.8%
### Performance Per Tag 🏷️
| Tag | Precision | Recall | Average Precision (AP) |
| ------------------------------------ | --------- | ------ | ---------------------- |
| **1982 Donruss** | 100.0% | 100.0% | 100.0% |
| **1984 Topps** | 100.0% | 100.0% | 100.0% |
| **1987 Fleer** | 95.5% | 95.5% | 99.8% |
| **1987 Topps** | 100.0% | 100.0% | 100.0% |
| **1988 Donruss** | 95.5% | 100.0% | 100.0% |
| **1988 Donruss Pack** | 100.0% | 100.0% | 100.0% |
| **1988 Fleer** | 100.0% | 85.7% | 100.0% |
| **1988 Fleer Pack** | 100.0% | 100.0% | 100.0% |
| **1988 Score** | 100.0% | 100.0% | 100.0% |
| **1988 Topps** | 96.0% | 100.0% | 99.7% |
| **1988 Topps Pack** | 100.0% | 100.0% | 100.0% |
| **1989 Bowman** | 100.0% | 100.0% | 100.0% |
| **1989 Donruss** | 100.0% | 100.0% | 100.0% |
| **1989 Donruss Pack** | 100.0% | 100.0% | 100.0% |
| **1989 Fleer** | 100.0% | 100.0% | 100.0% |
| **1989 Score** | 100.0% | 100.0% | 100.0% |
| **1989 Topps** | 95.2% | 95.2% | 99.6% |
| **1989 Topps Pack** | 100.0% | 91.7% | 100.0% |
| **1989 Upper Deck** | 100.0% | 100.0% | 100.0% |
| **1990 Donruss** | 96.2% | 100.0% | 100.0% |
| **1990 Donruss Pack** | 100.0% | 100.0% | 100.0% |
| **1990 Fleer** | 100.0% | 96.9% | 99.7% |
| **1990 Fleer Pack** | 100.0% | 100.0% | 100.0% |
| **1990 Leaf** | 100.0% | 100.0% | 100.0% |
| **1990 Leaf Pack** | 100.0% | 100.0% | 100.0% |
| **1990 Topps** | 100.0% | 100.0% | 100.0% |
| **1990 Upper Deck High Series Pack**| 100.0% | 100.0% | 100.0% |
| **1991 Donruss Series 1 Pack** | 71.4% | 100.0% | 100.0% |
| **1991 Donruss Series 2 Pack** | 100.0% | 100.0% | 100.0% |
| **1991 Fleer** | 100.0% | 100.0% | 100.0% |
| **1991 Fleer Ultra** | 100.0% | 100.0% | 100.0% |
| **1991 Leaf** | 100.0% | 95.5% | 95.5% |
| **1991 Leaf Pack** | 100.0% | 25.0% | 100.0% |
| **1991 Score** | 100.0% | 100.0% | 100.0% |
| **1991 Topps** | 100.0% | 100.0% | 100.0% |
| **1991 Topps Pack** | 100.0% | 100.0% | 100.0% |
| **1991 Upper Deck** | 100.0% | 89.5% | 99.5% |
| **1991 Upper Deck Low Series Pack**| 100.0% | 100.0% | 100.0% |
| **1992 Donruss Series 2 Pack** | 100.0% | 100.0% | 100.0% |
| **1992 Fleer** | 95.5% | 100.0% | 100.0% |
| **1992 Fleer Pack** | 100.0% | 75.0% | 100.0% |
| **1992 Fleer Ultra** | 100.0% | 100.0% | 100.0% |
| **1992 Leaf** | 100.0% | 100.0% | 100.0% |
| **1992 O-Pee-Chee Premiere** | 100.0% | 100.0% | 100.0% |
| **1992 Pinnacle** | 94.7% | 100.0% | 99.7% |
| **1992 Pinnacle Pack** | 100.0% | 100.0% | 100.0% |
| **1992 Upper Deck** | 95.8% | 95.8% | 95.7% |
| **1992 Upper Deck High Series Pack**| 100.0% | 100.0% | 100.0% |
| **1993 Fleer** | 95.2% | 100.0% | 100.0% |
| **1993 Fleer Series 1 Pack** | 100.0% | 100.0% | 100.0% |
| **1993 Fleer Series 2 Pack** | 100.0% | 100.0% | 100.0% |
| **1993 Topps** | 91.3% | 95.5% | 99.6% |
| **1994 Leaf** | 100.0% | 100.0% | 100.0% |
| **1994 Pinnacle** | 100.0% | 100.0% | 100.0% |
| **1994 Score** | 100.0% | 100.0% | 100.0% |
| **1995 Leaf** | 100.0% | 100.0% | 100.0% |
| **1995 Select** | 100.0% | 100.0% | 100.0% |
| **1996 Pinnacle** | 100.0% | 100.0% | 100.0% |
## Repository Structure 🗂
- `model` - Contains the ONNX and TensorFlow model files.
- `src` - Example projects demonstrating how to use the models.
## How to Use 🛠️
1. Clone this repository to your local machine.
```bash
git clone https://github.com/JunkWaxData/JunkWaxDetection.git
```
2. Navigate to the `src` folder for example code in various programming languages.
3. Load the model in your preferred framework and integrate it into your project.
## Example Frameworks 💻
- **Python (ONNXRuntime)**
- **C# (ML.NET)**
- **JavaScript (TensorFlow\.js)**
Feel free to explore the `src` folder for detailed implementation examples. Contributions in other languages are encouraged!
## Contributing 🤝
We encourage community contributions! Whether it's submitting your own example project or improving documentation, we welcome your input.
## License 📄
This project is licensed under the [MIT License](LICENSE). By contributing, you agree to license your work under the same terms.
## Contact 📬
For any inquiries, please reach out to us at [**[email protected]**](mailto\:[email protected]).