In this study, we present the implementation of a U-Net architecture from scratch, aimed at solving image segmentation tasks. U-Net, originally designed for biomedical image segmentation, is known for its symmetric encoder-decoder structure and skip connections, enabling precise localization while preserving context. Our implementation showcases the design, training, and evaluation of the network, highlighting its effectiveness in segmenting images. We conducted experiments on publicly available datasets and demonstrated the ability of our U-Net model to achieve high segmentation accuracy with minimal computational overhead. This report provides insights into the methodology, experimental setup, and results, paving the way for future improvements and applications.
Image segmentation, a critical task in computer vision, involves partitioning an image into meaningful regions to identify objects or boundaries. U-Net has emerged as a popular architecture due to its simplicity and effectiveness, particularly in the medical imaging domain. This report focuses on the implementation of U-Net from scratch, detailing the challenges, design choices, and performance evaluation. By building the network manually, we aim to deepen the understanding of its underlying principles and explore its adaptability to various datasets.
The U-Net model consists of an encoder-decoder architecture with skip connections. The encoder captures high-level features using convolutional and pooling layers, while the decoder reconstructs the spatial dimensions using upsampling layers. Skip connections bridge the encoder and decoder, preserving fine-grained details. Our implementation involves the following steps:
1.Data Preprocessing: Input images were resized and normalized. Ground truth masks were prepared to match the input image dimensions.
2.Model Design: The U-Net was built with convolutional layers, ReLU activations, max pooling for downsampling, and transposed convolutions for upsampling.
3.Loss Function: We used a combination of Binary Cross-Entropy to optimize segmentation accuracy.
4.Optimization: The model was trained using the Adam optimizer with a learning rate scheduler to improve convergence.
We conducted experiments on the VOC Segmentation Dataset to validate our implementation. The dataset was split into training, validation, and testing subsets. Augmentation techniques such as rotation, flipping, and scaling were applied to improve generalization. The model was trained for 4 epochs, with periodic evaluations on the validation set.
This study demonstrates the successful implementation of U-Net from scratch, reaffirming its utility in image segmentation tasks. The architecture’s simplicity and robustness make it a versatile choice for various applications. Future work could involve optimizing the architecture for real-time segmentation, experimenting with different loss functions, and applying the model to diverse datasets. The insights gained from this implementation provide a foundation for further exploration and advancements in segmentation algorithms.
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