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| | | #Darknet# |
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| | | Darknet is an open source neural network framework written in C and CUDA. It is fast, easy to install, and supports CPU and GPU computation. |
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| | | # Magic: The Gathering Card Detection Model |
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| | | For more information see the [Darknet project website](http://pjreddie.com/darknet). |
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| | | This is a fork of [Yolo-v3 and Yolo-v2 for Windows and Linux by AlexeyAB](https://github.com/AlexeyAB/darknet#how-to-compile-on-linux) for creating a custom model for [My MTG card detection project](https://github.com/hj3yoo/MTGCardDetector). |
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| | | For questions or issues please use the [Google Group](https://groups.google.com/forum/#!forum/darknet). |
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| | | ## Day ~0: Sep 6th, 2018 |
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| | | --------------------- |
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| | | Uploading all the progresses on the model training for the last few days. |
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| | | First batch of model training is completed, where I used ~40,000 generated images of MTG cards laid out in one of the pre-defined pattern. |
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| | | <img src="https://github.com/hj3yoo/darknet/blob/master/figures/0_training_set_example_1.jpg" width="600"> |
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| | | <img src="https://github.com/hj3yoo/darknet/blob/master/figures/0_training_set_example_1.jpg" width="600"> |
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| | | <img src="https://github.com/hj3yoo/darknet/blob/master/figures/0_training_set_example_1.jpg" width="600"> |
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| | | After 5000 training epochs, the model got 88% validation accuracy on the generated test set. |
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| | | <img src="https://github.com/hj3yoo/darknet/blob/master/figures/0_detection_result_1.jpg" width="600"> |
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| | | <img src="https://github.com/hj3yoo/darknet/blob/master/figures/0_detection_result_2.jpg" width="600"> |
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| | | <img src="https://github.com/hj3yoo/darknet/blob/master/figures/0_detection_result_3.jpg" width="600"> |
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| | | <img src="https://github.com/hj3yoo/darknet/blob/master/figures/0_detection_result_4.jpg" width="600"> |
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| | | However, there are some blind spots on the model, notably: |
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| | | - Fails to spot some of the obscured cards, where only a fraction of them are shown. |
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| | | - Fairly fragile against any glaring or light variations. |
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| | | - Cannot detect any skewed cards. |
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| | | Example of bad detections: |
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| | | <img src="https://github.com/hj3yoo/darknet/blob/master/figures/0_detection_result_5.jpg" width="600"> |
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| | | <img src="https://github.com/hj3yoo/darknet/blob/master/figures/0_detection_result_6.jpg" width="600"> |
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| | | <img src="https://github.com/hj3yoo/darknet/blob/master/figures/0_detection_result_7.jpg" width="600"> |
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| | | The second and third problems should easily be solved by further augmenting the dataset with random lighting and image skew. I'll have to think more about the first problem, though. |
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