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| | | # Magic: The Gathering Card Detection Model |
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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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| | | ## Day ~0: Sep 6th, 2018 |
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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="360"> <img src="https://github.com/hj3yoo/darknet/blob/master/figures/0_training_set_example_2.jpg" width="360"> <img src="https://github.com/hj3yoo/darknet/blob/master/figures/0_training_set_example_3.jpg" width="360"> |
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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="360"> <img src="https://github.com/hj3yoo/darknet/blob/master/figures/0_detection_result_2.jpg" width="360"> <img src="https://github.com/hj3yoo/darknet/blob/master/figures/0_detection_result_3.jpg" width="360"> <img src="https://github.com/hj3yoo/darknet/blob/master/figures/0_detection_result_4.jpg" width="360"> |
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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="360"> <img src="https://github.com/hj3yoo/darknet/blob/master/figures/0_detection_result_6.jpg" width="360"> <img src="https://github.com/hj3yoo/darknet/blob/master/figures/0_detection_result_7.jpg" width="360"> |
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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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| | | ## Sept 7th, 2018 |
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| | | Added several image augmentation techniques to apply to the training set: noise, dropout, light variation, and glaring: |
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| | | <img src="https://github.com/hj3yoo/darknet/blob/master/figures/1_augmented_set_example_1.jpg" width="360"> <img src="https://github.com/hj3yoo/darknet/blob/master/figures/1_augmented_set_example_2.jpg" width="360"> <img src="https://github.com/hj3yoo/darknet/blob/master/figures/1_augmented_set_example_3.jpg" width="360"> <img src="https://github.com/hj3yoo/darknet/blob/master/figures/1_augmented_set_example_4.jpg" width="360"> |
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| | | Currently trying to generate enough images to start model training. Hopefully this helps. |
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| | | Recompiled darknet with OpenCV and CUDNN installed, and recalculated anchors. |
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| | | ----------------------- |
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| | | I've ran a quick training with tiny_yolo configuration with new training data, and Voila! The model performs significantly better than the last iteration, even against some hard images with glaring & skew! The first prediction model can't detect anything from these new test images, so this is a huge improvement to the model :) |
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| | | <img src="https://github.com/hj3yoo/darknet/blob/master/figures/1_detection_result_1.jpg" width="360"> <img src="https://github.com/hj3yoo/darknet/blob/master/figures/1_decision_result_2.jpg" width="360"> <img src="https://github.com/hj3yoo/darknet/blob/master/figures/1_decision_result_3.jpg" width="360"> <img src="https://github.com/hj3yoo/darknet/blob/master/figures/1_decision_result_4.jpg" width="360"> <img src="https://github.com/hj3yoo/darknet/blob/master/figures/1_decision_result_5.jpg" width="360"> <img src="https://github.com/hj3yoo/darknet/blob/master/figures/1_decision_result_6.jpg" width="360"> |
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| | | <img src="https://github.com/hj3yoo/darknet/blob/master/figures/1_learning_curve.jpg" width="640"> |
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| | | The video demo can be found here: https://www.youtube.com/watch?v=kFE_k-mWo2A&feature=youtu.be |
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| | | ## Sept 10th, 2018 |
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| | | I've been training a new model with a full YOLOv3 configuration (previous one used Tiny YOLOv3), and it's been taking a lot more resources: |
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| | | <img src="https://github.com/hj3yoo/darknet/blob/master/figures/2_learning_curve.jpg" width="640"> |
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| | | The author of darknet did mention that full network will take significantly more training effort, so I'll just have to wait. At this rate, it should reach 50k epoch in about a week :/ |
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| | | ## Sept 13th, 2018 |
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| | | The training for full YOLOv3 model has turned sour - the loss saturated around 0.45, and didn't seem like it would improve in any reasonable amount of time. |
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| | | <img src="https://github.com/hj3yoo/darknet/blob/master/figures/3_learning_curve.jpg" width="640"> |
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| | | As expected, the performance of the model with 0.45 loss was fairly bad. Not to mention that it's quite slower, too. I've decided to continue with tiny YOLOv3 weights. I tried to train it further, but it was already saturated, and was the best it could get. |
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| | | --------------------- |
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| | | Bad news, I couldn't find any repo that has python wrapper for darknet to pursue this project further. There is a [python example](https://github.com/AlexeyAB/darknet/blob/master/darknet.py) in the original repo of this fork, but [it doesn't support video input](https://github.com/AlexeyAB/darknet/issues/955). Other darknet repos are in the same situation. |
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| | | I suppose there is a poor man's alternative - feed individual frames from the video into the detection script for image. I'll have to give it a shot. |
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| | | ## Sept 14th, 2018 |
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| | | Thankfully, OpenCV had an implementation for DNN, which supports YOLO as well. They have done quite an amazing job, and the speed isn't too bad, either. I can score about 20~25fps on my tiny YOLO, without using GPU. |
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| | | ## Sept 15th, 2018 |
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| | | I tried to do an alternate approach - instead of making model identify cards as annonymous, train the model for EVERY single card. As you may imagine, this isn't sustainable for 10000+ different cards that exists in MTG, but I thought it would be reasonable for classifying 10 different cards. |
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| | | Result? Suprisingly effective. |
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| | | <img src="https://github.com/hj3yoo/darknet/blob/master/figures/4_detection_result_1.jpg" width="360"> <img src="https://github.com/hj3yoo/darknet/blob/master/figures/4_detection_result_2.jpg" width="360"><img src="https://github.com/hj3yoo/darknet/blob/master/figures/4_detection_result_3.jpg" width="360"> <img src="https://github.com/hj3yoo/darknet/blob/master/figures/4_detection_result_4.png" width="360"> |
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| | | They're of course slightly worse than annonymous detection and impractical for any large number of cardbase, but it was an interesting approach. |
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| | | ------------------ |
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| | | I've made a quick openCV algorithm to extract cards from the image, and it works decently well: |
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| | | <img src="https://github.com/hj3yoo/darknet/blob/master/figures/4_detection_result_5.jpg" width="360"> |
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| | | At the moment, it's fairly limited - the entire card must be shown without obstruction nor cropping, otherwise it won't detect at all. |
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| | | Unfortunately, there is very little use case for my trained network in this algorithm. It's just using contour detection and perceptual hashing to match the card. |
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| | | ## Sept 16th, 2018 |
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| | | I've tweaked the openCV algorithm from yesterday and ran for a demo: |
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| | | https://www.youtube.com/watch?v=BZkRZDyhMRE&feature=youtu.be |
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| | | ## Oct 4th, 2018 |
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| | | With the current model I have, there seems to be little hope - I simply don't have enough knowledge in classical CV technique to separate overlaying cards. Even if I could, perceptual hash will be harder to use if I were to use only a fraction of a card image to classify it. |
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| | | There is an alternative to venture into instance segmentation with [mask R-CNN](https://arxiv.org/pdf/1703.06870.pdf), at the cost of losing real-time processing speed (and considerably more development time). Maybe worth a shot, although I'd have to nearly start from scratch (other than training data generation). |
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| | | ## Oct 10th, 2018 |
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| | | I've been trying to fiddle with the mask R-CNN using [this repo](https://github.com/matterport/Mask_RCNN)'s implementation, and got to train them with 60 manually labelled image set. The result is not too bad considering such a small dataset was used. However, there was a high FP rate overall (again, probably because of small dataset and the simplistic features of cards). |
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| | | <img src="https://github.com/hj3yoo/mtg_card_detector/blob/master/figures/5_rcnn_result_1.png" width="360"><img src="https://github.com/hj3yoo/mtg_card_detector/blob/master/figures/5_rcnn_result_2.png" width="360"><img src="https://github.com/hj3yoo/mtg_card_detector/blob/master/figures/5_rcnn_result_3.png" width="360"><img src="https://github.com/hj3yoo/mtg_card_detector/blob/master/figures/5_rcnn_result_4.png" width="360"><img src="https://github.com/hj3yoo/mtg_card_detector/blob/master/figures/5_rcnn_result_5.png" width="360"> |
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| | | Although it may be worth to generate large training dataset and train the model more thoroughly, I'm being short on time, as there are other priorities to do. I may revisit this later. I will be cleaning this repo in the next few days, wrapping it up for now. |
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