Semantic segmentation is a fundamental task in computer vision that aims to classify every pixel in an image into different categories. Deep convolutional neural networks (CNNs) have achieved state-of-the-art results in semantic segmentation. Deeplabv3+ is a deep CNN-based model that uses atrous convolution and a decoder network to improve the accuracy of semantic segmentation. In this research, we conduct an ablation study on Deeplabv3+ to analyze the importance of its different components and their impact on the performance of the model, which provides valuable insights for developing more efficient and accurate semantic segmentation models. Our study encompasses a comprehensive examination of Deeplabv3+. We explore its constituent elements, including the backbone network, the Atrous Spatial Pyramid Pooling (ASPP) module, and the decoder network. Our investigation delves into the reasons underlying performance changes resulting from the removal of these architectural components. This analysis provides a deeper understanding of their intrinsic roles in shaping the model’s segmentation efficacy. Notably, we identify that the backbone exerts a substantial impact. Changes to other components yield relatively minor effects, while modifications to the backbone wield a remarkable influence. The Encoder-decoder structure also bears significant weight, playing a pivotal role in the upsampling process. This structure significantly impacts precision, enhancing boundary clarity and positional accuracy. Moreover, we recognize the vital role of feature integration. Features aid in establishing pixel position information, enhancing boundary definition, and positioning accuracy. Furthermore, the ASPP module emerges as a critical factor. ASPP leverages multi-scale information to differentiate complex object boundaries, further enriching the model’s semantic understanding.

Automatic food recognition is becoming a solution to perform diet control as it has the ability to release the burden of self diet assessment by offering an easy process that immediately detects the food elements in the picture. This step consisting of accurately segmenting the different areas into the proper food category is crucial to make an accurate calorie estimation. In this thesis, we utilize the PREVENT project as a background to the task of creating a model capable of segmenting food. We decided to carry out the research on a newly annotated dataset FoodSeg103 that consists of a more data-realistic support for the implementation of this study. Most papers performed on FoodSeg103 focus on Vision transformer models that are seen as very trendy but also with computational constraints. We decided to choose DeepLabV3 as a dilation-based semantic segmentation model with main objective of training the model on the dataset and additionally with hope of improving the state-of-the-art results. We set up an iterative optimization process with purpose of maximizing the results and managed to attain 48.27% mIOU (also mentioned as "mIOU all" in the thesis). We also obtained a significant difference in average mIOU troughout all random search experiments in comparison to bayesian optimization experiments.This study has not overpassed the state-of-the-art performance but has managed to settle 1% behind, BEIT v2 Large remaining in first position with 49.4% mIOU.