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Identifikace člověka podle fotografie dlaně / hřbetu ruky
Štanga, Miroslav ; Vaško, Marek (referee) ; Herout, Adam (advisor)
This work focuses on using contrastive self-supervised learning method for creating model of deep learning intended for person recognition based on hand photographs. The paper outlines fundamentals of machine learning, utilized tools and dataset. The method was developed using PyTorch library. The proposed model draws inspiration from the SimCLR architecture and its use of contrastive representation learning. The proposed approach utilizes the triplet loss function for optimization. Then the optimization process is described and impact of individual hyperparameters on the model´s accuracy is compared. The resulting model was trained on 1696 hand photos and achieves 98% accuracy on validation set. The accuracy achieved using self-supervised methods is higher than the accuracy achieved using supervised methods.
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Graffiti Tags Re-Identification
Pavlica, Jan ; Beran, Vítězslav (referee) ; Špaňhel, Jakub (advisor)
This thesis focuses on the possibility of using current methods in the field of computer vision to re-identify graffiti tags. The work examines the possibility of using convolutional neural networks to re-identify graffiti tags, which are the most common type of graffiti. The work experimented with various models of convolutional neural networks, the most suitable of which was MobileNet using the triplet loss function, which managed to achieve a mAP of 36.02%.
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Self-Supervised Learning for Recognition of Hand Poses in Image
Makaiová, Lucia ; Kocur, Viktor (referee) ; Herout, Adam (advisor)
This work focuses on using self-supervised learning for the task of hand poses recognition in image. I have used contrastive method of self-supervised learning and optimized the solution iteratively, using techniques such as early stopping, triplet mining, optimization of hyperparameters or experimenting with various model architectures. The method was implemented with Pytorch framework and Tensorboard was used for data processing and visualization. I have trained the first model using a supervised method, to obtain reference values. I have successfully matched this reference result by training a self-supervised model on Handz dataset and achieving 83% accuracy. The created solution provides findings, which can be applied to similar problems, such as recognition of sport poses. The main contribution of this work is the discovery, that self-supervised methods are particularly effective when using a labeled dataset for downstream task with just a small amount of samples, which in addition have uneven distribution of samples for individual classes. Based on these findings, it is possible to create a method for self-supervised learning for recognition of sport poses or further optimize existing solution for hand poses.
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Graffiti Tags Re-Identification
Pavlica, Jan ; Beran, Vítězslav (referee) ; Špaňhel, Jakub (advisor)
This thesis focuses on the possibility of using current methods in the field of computer vision to re-identify graffiti tags. The work examines the possibility of using convolutional neural networks to re-identify graffiti tags, which are the most common type of graffiti. The work experimented with various models of convolutional neural networks, the most suitable of which was MobileNet using the triplet loss function, which managed to achieve a mAP of 36.02%.
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