Sökning: "Domain Randomization"

Visar resultat 1 - 5 av 7 uppsatser innehållade orden Domain Randomization.

1. 1. Using Synthetic Data For Object Detection on the edge in Hazardous Environments

   Uppsats för yrkesexamina på avancerad nivå, Lunds universitet/Institutionen för reglerteknik

   Författare :Faraz Azarnoush; Damil Sabotic; [2024]
   Nyckelord :Technology and Engineering;

   Sammanfattning : This thesis aims to evaluate which aspects are important when generating synthetic data with the purpose of running on a lightweight object detection model on an edge device. The task we constructed was to detect Canisters and whether they feature a protective valve called a Cap or not (called a No-Cap). LÄS MER

3. 2. Sim2Real: Generating synthetic images from industry CAD models with domain randomization

   Master-uppsats, Uppsala universitet/Institutionen för informationsteknologi

   Författare :Duruo Li; [2023]
   Nyckelord :;

   Sammanfattning : Deep learning methods for computer vision applications require massive visual data for model training. Although it is possible to utilize public datasets such as ImageNet, MS COCO, and CIFAR-100, it becomes problematic when there is a need for more task-specific data when new training data collection typically is needed. LÄS MER

4. 3. Targeted Improvement of a Deep Learning Object Detector Using Synthetic Training Data

   Master-uppsats, Lunds universitet/Matematisk statistik

   Författare :Katja Palmkvist; Olivia Mattsson; [2022]
   Nyckelord :Machine Learning; Deep Learning; Object Detection; Computer Vision; Synthetic Data; 3D-rendering; Technology and Engineering;

   Sammanfattning : When working with object detection, the quality and quantity of the training data is often a recurrent bottleneck. This thesis proposes a technique of incrementally improving an object detector using synthetically rendered data. LÄS MER

5. 4. Domain Adaptation to Meet the Reality-Gap from Simulation to Reality

   Master-uppsats, Linköpings universitet/Datorseende

   Författare :Fanny Forsberg; [2022]
   Nyckelord :domain adaptation; reality gap; domain randomization; deep learning; autonomous robot;

   Sammanfattning : Being able to train machine learning models on simulated data can be of great interest in several applications, one of them being for autonomous driving of cars. The reason is that it is easier to collect large labeled datasets as well as performing reinforcement learning in simulations. LÄS MER

7. 5. Confounder Parsing for Text Matching

   Master-uppsats, Göteborgs universitet/Institutionen för data- och informationsteknik

   Författare :Jannes Reichl; Johan Rönkkö; [2021-07-06]
   Nyckelord :Text matching; confounder parsing; causal inference; covariate balance; International Monetary Fund; policy evaluation;

   Sammanfattning : In observational studies for policy evaluation, matching is used in service of causal inference to simulate randomization and thus reduce selection bias that might occur when treatment assignment differs systematically. This is done by balancing the distribution of confounding covariates measured before treatments. LÄS MER