Update README.md

README.md

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-license: lgpl-3.0
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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
+
+