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Self-supervised learning in computer vision applications
Vančo, Timotej ; Richter, Miloslav (referee) ; Janáková, Ilona (advisor)
The aim of the diploma thesis is to make research of the self-supervised learning in computer vision applications, then to choose a suitable test task with an extensive data set, apply self-supervised methods and evaluate. The theoretical part of the work is focused on the description of methods in computer vision, a detailed description of neural and convolution networks and an extensive explanation and division of self-supervised methods. Conclusion of the theoretical part is devoted to practical applications of the Self-supervised methods in practice. The practical part of the diploma thesis deals with the description of the creation of code for working with datasets and the application of the SSL methods Rotation, SimCLR, MoCo and BYOL in the role of classification and semantic segmentation. Each application of the method is explained in detail and evaluated for various parameters on the large STL10 dataset. Subsequently, the success of the methods is evaluated for different datasets and the limiting conditions in the classification task are named. The practical part concludes with the application of SSL methods for pre-training the encoder in the application of semantic segmentation with the Cityscapes dataset.
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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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Pedestrian Attribute Analysis
Studená, Zuzana ; Špaňhel, Jakub (referee) ; Hradiš, Michal (advisor)
This work deals with obtaining pedestrian information, which are captured by static, external cameras located in public, outdoor or indoor spaces. The aim is to obtain as much information as possible. Information such as gender, age and type of clothing, accessories, fashion style, or overall personality are obtained using using convolutional neural networks. One part of the work consists of creating a new dataset that captures pedestrians and includes information about the person's sex, age, and fashion style. Another part of the thesis is the design and implementation of convolutional neural networks, which classify the mentioned pedestrian characteristics. Neural networks evaluate pedestrian input images in PETA, FashionStyle14 and BUT Pedestrian Attributes datasets. Experiments performed over the PETA and FashionStyle datasets compare my results to various convolutional neural networks described in publications. Further experiments are shown on created BUT data set of pedestrian attributes.
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Reconstruction of Facial Images Using Neural Networks
Zubalík, Petr ; Drahanský, Martin (referee) ; Goldmann, Tomáš (advisor)
The main purpose of this bachelor's thesis is to propose and implement a model, using neural networks, that will be able to reconstruct low-resolution facial images with blurry parts of the face. The task of super-resolution of facial images is solved by two models based on convolutional neural networks. The first model is built upon the architecture of ResNet whereas the other model makes use of the principles of generative adversarial networks. The proposed models are implemented in the Python programming language with the use of application programming interface of the TensorFlow framework. Moreover, as a part of this work, an application with a simple grafical user interface was created. This application makes it easy to use the implemented models. Several experiments are analyzed in the last chapter of this thesis to evaluate the performance of the models.
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Deep neural network for supercomputer environments
Bronda, Samuel ; Kolařík, Martin (referee) ; Burget, Radim (advisor)
The main benefit of the work is the optimization of the hardware configuration for the calculation of neural networks. The theoretical part describes neural networks, deep learning frameworks and hardware options. The next part of the thesis deals with implementation of performance tests, which include application of Inception V3 and ResNet models. Network models are applied to various graphics cards and computing hardware. The output of the thesis is the implemented model of the network Inception V3, which examines the graphics cards and their performance, time-consuming calculations and their efficiency. The ResNet model is applied to a section that examines other impacts on neural network computing such as used disk, operating memory, and so on. Each practical part contains a discussion where the knowledge of the given part is explained. In the case of consumption measurement, a mismatch between the declaration by the manufacturer and the measured values was identified.
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Vehicle License Plate Detection and Recognition Software
Masaryk, Adam ; Hradiš, Michal (referee) ; Špaňhel, Jakub (advisor)
The aim of this bachelor thesis is to design and develop software that can detect and recognize license plates from images. The software is divided into 3 parts - license plates detection, detector output processing and license plates characters recognition. We decided to implement detection and recognition using modern methods using convolutional neural networks.
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