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| license: lgpl-3.0 |
| language: |
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| pipeline_tag: image-feature-extraction |
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| # Model Card for BoardCNN |
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| BoardCNN implements a Convolutional Neural Network (CNN) to recognize the position from images of chess boards. |
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| The model expects a board image as input and returns the expected positions of the pieces on the board. |
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| ## Model Details |
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| Custom CNN architecture was implemented via pytorch |
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| **Developed by:** Igor Alexey <br> |
| **Model type:** Safetensors <br> |
| **License:** GNU GPL v3 <br> |
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| ### Model Sources |
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| - **Repository:** [More Information Needed] |
| - **Demo:** [More Information Needed] |
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| ## Uses |
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| The model can be used to make predictions on new chess board images. The output is a 8x8 grid of chess piece symbols, representing the predicted position of pieces on the board. |
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| ### Out-of-Scope Use |
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| The pre-trained models are not made for scanning 3D boards, although it's likely the architecture should scale well for this task with a proper training set. |
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| ## Limitations |
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| Might not always give 100% correct output, especially on varying piece sets and board themes. |
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| ## Getting started |
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| Use the code below to get started with the model. |
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| [More Information Needed] |
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| ## Training Details |
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| ### Training Data |
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| The models are trained on 5k gnerated images of valid random board positions with reasonable piece sets from lichess. |
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| ### Training Procedure |
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| <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
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| #### Training Hyperparameters |
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| - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> |
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| #### Speeds, Sizes, Times [optional] |
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| <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> |
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| [More Information Needed] |
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| ## Evaluation |
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| <!-- This section describes the evaluation protocols and provides the results. --> |
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| ### Testing Data, Factors & Metrics |
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| #### Testing Data |
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| <!-- This should link to a Dataset Card if possible. --> |
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| [More Information Needed] |
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| #### Factors |
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| <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
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| [More Information Needed] |
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| #### Metrics |
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| <!-- These are the evaluation metrics being used, ideally with a description of why. --> |
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| [More Information Needed] |
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| ### Results |
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| [More Information Needed] |
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| #### Summary |
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