Building object detection system, image classification and image segmentation models using Pytorch, CNN, YOLOv, and SSD
β±οΈ Length: 3.2 total hours
β 4.75/5 rating
π₯ 1,714 students
π June 2025 update
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- Course Overview
- This comprehensive and hands-on course serves as your gateway into the dynamic world of computer vision, focusing specifically on two pivotal areas: object detection and image classification. It’s meticulously crafted to transform beginners into proficient practitioners, capable of designing and deploying advanced AI models.
- Dive deep into the core theoretical underpinnings and practical implementation strategies required to build intelligent systems that can perceive and interpret visual data, much like human vision.
- Explore the cutting-edge methodologies and architectures that power modern computer vision applications, from pinpointing multiple objects within an image to categorizing entire scenes accurately.
- Benefit from an updated curriculum, reflecting the latest advancements and best practices in the field as of June 2025, ensuring your skills remain current and relevant in a rapidly evolving tech landscape.
- Understand the practical applications of these technologies across various industries, from autonomous vehicles and surveillance to medical imaging and quality control.
- Engage with a structured learning path that prioritizes practical application over abstract theory, enabling you to build tangible projects and reinforce your understanding through direct implementation.
- Requirements / Prerequisites
- Foundational Python Proficiency: A working knowledge of Python programming, including data types, control flow, functions, and basic object-oriented concepts, is essential to follow the coding exercises effectively.
- Basic Machine Learning Concepts: Familiarity with fundamental machine learning principles such as supervised learning, training/testing splits, overfitting, and model evaluation will provide a solid background.
- Conceptual Understanding of Deep Learning: While not strictly required to be an expert, a rudimentary grasp of what neural networks are and their purpose will be beneficial.
- Linear Algebra and Calculus (Conceptual): An intuitive understanding of concepts like vectors, matrices, and gradients (how models learn) is helpful, though deep mathematical proofs are not the focus.
- Computational Setup: Access to a computer with an internet connection. A GPU is highly recommended for faster model training and experimentation, though CPU-only environments can be used for smaller tasks.
- Command-Line Interface Comfort: Basic navigation and execution of commands in a terminal or command prompt environment.
- Skills Covered / Tools Used
- PyTorch Framework Mastery: Gain proficiency in using PyTorch for building, training, and evaluating complex deep learning models, leveraging its dynamic computational graph and extensive ecosystem.
- Convolutional Neural Network (CNN) Architectures: Implement and understand various CNN structures, including their layers (convolutional, pooling, fully connected) and their role in feature extraction for image-based tasks.
- Advanced Model Architectures: Critically analyze and apply state-of-the-art object detection models such as SSD, Faster R-CNN, YOLO variants, and DETR (DEtection TRansformer) for diverse scenarios.
- Data Preprocessing and Augmentation: Develop strategies for preparing image datasets, including resizing, normalization, and advanced augmentation techniques crucial for improving model robustness and generalization.
- Performance Metrics and Evaluation: Learn to interpret and utilize key metrics like Intersection over Union (IoU), Mean Average Precision (mAP), F1-score, and accuracy to thoroughly evaluate model performance.
- Transfer Learning Techniques: Master the art of leveraging pre-trained models on large datasets (like ImageNet) to quickly adapt and fine-tune them for specific custom object detection and classification tasks.
- Loss Function Customization: Explore and implement various loss functions (e.g., cross-entropy, focal loss, smooth L1 loss) tailored for bounding box regression and classification components of detection systems.
- Model Optimization and Hyperparameter Tuning: Acquire skills in optimizing model training processes through judicious selection of optimizers, learning rates, batch sizes, and other hyperparameters.
- Real-World Application Development: Apply learned concepts to practical problems, including the specialized task of manufacturing defect detection, showcasing the adaptability of these CV models.
- Deep Learning Toolkits: Utilize PyTorch primarily, with exposure to Keras and TensorFlow (via Keras) for specific applications, understanding the versatility of different frameworks.
- Debugging and Troubleshooting: Develop systematic approaches to diagnose and resolve common issues encountered during the development and training of deep learning computer vision models.
- Introduction to Image Segmentation Concepts: While the primary focus is on detection and classification, gain an introductory understanding of how these concepts extend to image segmentation.
- Benefits / Outcomes
- Develop Portfolio-Ready Projects: Emerge from the course with practical, hands-on projects that you can showcase to potential employers, demonstrating your ability to build functional computer vision systems.
- Independent Problem-Solving: Gain the confidence and skills to independently tackle complex image recognition and object detection challenges in real-world scenarios.
- Career Advancement: Position yourself for roles in AI, Machine Learning Engineering, Data Science, and Computer Vision Development, equipped with in-demand technical expertise.
- Deepened Conceptual Understanding: Move beyond mere tool usage to a profound understanding of how and why various object detection and image classification algorithms work, enabling informed decision-making.
- Stay Ahead of the Curve: With content updated to June 2025, you will be proficient in using the latest and most relevant tools and techniques in the computer vision domain.
- Optimized Model Deployment: Learn foundational principles for considering how object detection models move from development to a production environment.
- Effective Model Selection: Develop an intuitive sense for choosing the most appropriate object detection or image classification architecture for a given task, balancing accuracy, speed, and resource constraints.
- Enhanced Debugging Capabilities: Improve your ability to identify and resolve issues in deep learning pipelines, a critical skill for any AI practitioner.
- PROS
- High Student Satisfaction: Boasts an impressive 4.75/5 rating from over 1,700 students, indicating a highly valued and effective learning experience.
- Up-to-Date Content: The June 2025 update ensures learners are exposed to current best practices and model architectures, keeping skills relevant.
- Diverse Model Coverage: Teaches implementation of multiple leading object detection frameworks (SSD, Faster R-CNN, YOLO, DETR), offering a comprehensive toolkit.
- Practical Application Focus: Strong emphasis on building systems, including a manufacturing defect detection model, showcasing real-world utility.
- PyTorch Centric: Utilizes PyTorch, a leading and highly flexible deep learning framework widely used in research and industry.
- CONS
- Concise Duration: At 3.2 total hours, the course might be quite fast-paced, potentially limiting exhaustive deep dives into the nuances of each advanced topic or model.
Learning Tracks: English,Development,Data Science
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