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--- |
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license: lgpl-3.0 |
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language: |
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- en |
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pipeline_tag: image-feature-extraction |
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--- |
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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> |
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**Model type:** Safetensors <br> |
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**License:** GNU GPL v3 <br> |
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### Model Sources |
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- **Repository:** [More Information Needed] |
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- **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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