This is a fork of Yolo-v3 and Yolo-v2 for Windows and Linux by AlexeyAB for creating a custom model for My MTG card detection project.
Uploading all the progresses on the model training for the last few days.
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.
After 5000 training epochs, the model got 88% validation accuracy on the generated test set.
However, there are some blind spots on the model, notably:
Example of bad detections:
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.
Added several image augmentation techniques to apply to the training set: noise, dropout, light variation, and glaring:
Currently trying to generate enough images to start model training. Hopefully this helps.
Recompiled darknet with OpenCV and CUDNN installed, and recalculated anchors.
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 :)
The video demo can be found here: https://www.youtube.com/watch?v=kFE_k-mWo2A&feature=youtu.be
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:
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 :/
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.
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.
Bad news, I couldn't find any repo that has python wrapper for darknet to pursue this project further. There is a python example in the original repo of this fork, but it doesn't support video input. Other darknet repos are in the same situation.
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.
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.
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.
Result? Suprisingly effective.
They're of course slightly worse than annonymous detection and impractical for any large number of cardbase, but it was an interesting approach.
I've made a quick openCV algorithm to extract cards from the image, and it works decently well:
At the moment, it's fairly limited - the entire card must be shown without obstruction nor cropping, otherwise it won't detect at all.
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.
