diplomsko delo
Abstract
Najbolj ključen dejavnik pri delovanju globokih biometričnih modelov je njihov postopek učenja. Diplomsko delo raziskuje različne pristope k optimizaciji učenja globokih nevronskih mrež, z namenom izboljšave njihove klasifikacijske točnosti. Osredotočamo se na metode iz področja zmanjšanja prileganja podatkom in vpliva različnih hiperparametrov na rezultate učenja. Za raziskavo uporabimo modele naučene na podatkovni zbirki ImageNet, ki jih s pomočjo prenosnega učenja prilagodimo za klasifikacijo ljudi na podlagi njihovega uhlja. Zaradi vpliva strojne opreme, testiramo tudi čas učenja posameznih modelov, ter povprečne hitrosti njihovih napovedi. Ugotavljamo, da je za našo učno množico najbolj primeren model ResNet18, z najvišjo točnostjo 56 odstotkov, sledi pa mu GoogLeNet z 51 odstotki.
Keywords
prenosno učenje;prileganje podatkov;regularizacija;augmentacija;ImageNet;razpoznava uhljev;biometrija uhljev;visokošolski strokovni študij;diplomske naloge;
Data
Language: |
Slovenian |
Year of publishing: |
2023 |
Typology: |
2.11 - Undergraduate Thesis |
Organization: |
UL FRI - Faculty of Computer and Information Science |
Publisher: |
[K. Štefe] |
UDC: |
004.93:57.087.1(043.2) |
COBISS: |
162224131
|
Views: |
53 |
Downloads: |
20 |
Average score: |
0 (0 votes) |
Metadata: |
|
Other data
Secondary language: |
English |
Secondary title: |
Optimizing the training of deep biometric models in ear biometrics |
Secondary abstract: |
The key factor in the performance of deep biometric models lies in their learning process. This study investigates different approaches to optimize the learning of deep neural networks, aiming to enhance their classification accuracy. We focus on methods that reduce overfitting and examine the impact of various hyperparameters. To conduct this research, we utilize models trained on the ImageNet dataset, which we fine-tune using transfer learning to classify people based on their ears. Furthermore, we assess the training times and average prediction speeds of individual models, considering hardware constraints. The results show that ResNet18 is the most suitable model for our training data, achieving best accuracy of 56%, closely followed by GoogLeNet with 51% |
Secondary keywords: |
neural networks;classification;transfer learning;data fitting;regularization;augmentation;ImageNet;ear recognition;ear biometrics;computer science;diploma;Biometrija;Biometrična identifikacija;Nevronske mreže (računalništvo);Računalniški vid;Računalništvo;Univerzitetna in visokošolska dela; |
Type (COBISS): |
Bachelor thesis/paper |
Study programme: |
1000470 |
Embargo end date (OpenAIRE): |
1970-01-01 |
Thesis comment: |
Univ. v Ljubljani, Fak. za računalništvo in informatiko |
Pages: |
46 str. |
ID: |
21439461 |