Augmented Startups – U-Net Pro Course

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U-Net Pro Course: Master the Art of Object Segmentation
U-Net Segmentation Specialization: Elevate Your Skills in Just 4 Weeks
Dive deep into the world of U-Net object segmentation with our comprehensive U-Net Pro Course. Designed to transform you into a specialist in just four weeks, this course covers everything from the basics to advanced techniques, ensuring you gain the expertise needed to excel in semantic segmentation.
Course Overview

Introduction – U-Net Architecture Implementation: Kickstart your journey with a thorough understanding of the U-Net architecture and its implementation.
Training – U-Net Data Processing and Augmentation: Learn the intricacies of data processing and augmentation techniques essential for training U-Net models.
Inference – Data Loading, Prediction, and Evaluation: Master the steps involved in loading data, making predictions, and evaluating the performance of your U-Net models.
Advanced Topics – ResNet50, MobileNet V2, CBAM: Explore advanced topics such as integrating ResNet50 and MobileNet V2 with U-Net, and understanding the role of CBAM (Convolutional Block Attention Module).
Applications – Brain Tumor, Polyp, Road Pothole Segmentation, and More!: Apply your knowledge to real-world scenarios, including brain tumor, polyp, and road pothole segmentation.

Module Breakdown
Module 1: Introduction & Theory

What is Semantic Segmentation?: Understand the fundamental concept of semantic segmentation and its importance in computer vision.
What is U-Net?: Get introduced to U-Net, its architecture, and why it stands out in the field of segmentation.
Effectiveness of U-Net: Explore the reasons behind U-Net’s effectiveness and its applications.
Architecture Comparison: Compare U-Net with other architectures to understand its unique advantages.
Performance Comparison: Analyze the performance of U-Net against other models to appreciate its strengths.
Why U-Net?: Discover the specific reasons why U-Net is the go-to choice for many segmentation tasks.

Module 2: U-Net Implementation

Going through the Research Paper: Delve into the original U-Net research paper to understand its inception and development.
Encoder Block Implementation: Learn how to implement the encoder block, a critical component of the U-Net architecture.
Decoder Block Implementation: Master the implementation of the decoder block, which is essential for reconstructing the segmented image.
UNET Model: Build and understand the complete U-Net model from scratch.
Ubuntu & Colab: Get hands-on experience with U-Net implementation on both Ubuntu and Google Colab platforms.

Module 3: Dataset

Dataset Sources: Explore various sources where you can find standard datasets for U-Net training.
Creating Dataset: Learn the process of creating your own dataset, including image collection and annotation.
Dataset Management: Understand best practices for managing and organizing your dataset.
Dealing with Faulty Images: Discover techniques to handle and clean faulty images in your dataset.
Cloud Storage Options: Explore options for storing your dataset in the cloud for easy access and scalability.

Module 4: Training

The Process of Training UNET: Walk through the entire training process, from data preparation to model training.
Dataset Processing: Learn how to preprocess your dataset to ensure optimal training performance.
Data Augmentation: Implement data augmentation techniques to enhance the robustness of your U-Net model.
UNET Model Training: Train your U-Net model on both Ubuntu and Google Colab.

Module 5: Evaluation & Deployment

Loading the Model: Learn how to load your trained U-Net model for evaluation and deployment.
Loading the Test Dataset: Prepare your test dataset for evaluating the model’s performance.
Predicting the Mask: Generate segmentation masks using your trained U-Net model.
Evaluating the Predicted Mask: Assess the quality of your predictions using metrics like F1 score, mIoU, precision, recall, and accuracy.
Calculating FPS: Measure the frame per second (FPS) performance of your U-Net model.

Module 6: Advanced Topics

Transfer Learning on UNET: Explore the application of transfer learning to enhance the performance of your U-Net model.
Attention Mechanism in UNET: Understand and implement attention mechanisms like CBAM to improve segmentation accuracy.
Convert Mask to Bounding Box: Learn how to convert segmentation masks into bounding boxes for object detection tasks.
UNET for Object Detection: Extend your U-Net skills to object detection, broadening your application scope.
Counting Objects Using UNET: Develop the ability to count objects in images using your U-Net model.

Join the U-Net Pro Course today and take your object segmentation skills to the next level!

Salepage: https://www.augmentedstartups.com/U-Net-Segmentation-Specialization-Course

Delivery Policy

When will I receive my course?

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