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