Best computer vision competitions on Kaggle (for beginners) | Summary and Q&A

TL;DR

A comprehensive guide to computer vision competitions on Kaggle for beginners, including datasets and recommended models.

Key Insights

• 👂 The list offers a comprehensive progression of computer vision competitions, starting with simple binary classification and advancing to more complex tasks like image segmentation and object detection.
• 🔶 Kaggle provides a platform to access large datasets, free TPUs, and a wide range of pre-existing models and notebooks for learning and inspiration.
• 😷 Each competition introduces unique challenges and learning opportunities, such as working with medical images, handling different data formats, and utilizing various deep learning models.
• 📔 Participants can benefit from studying existing notebooks, discussing approaches with the Kaggle community, and starting their own experimentation to improve results.
• 💗 Image augmentation techniques and using powerful GPUs or TPUs are important aspects to explore in computer vision competitions.
• 🤑 Progressing from simple datasets to more challenging ones allows beginners to build their skills and knowledge gradually.
• 💻 GANs offer an exciting avenue to explore in computer vision, enabling the generation of realistic images.

Transcript

hello everyone and welcome to my youtube channel in this video today i'm going to talk about some computer vision competitions on kaggle for beginners in my previous video i talked about natural language processing competitions on kaggle which i found were the best ones to learn from and these are the computer vision problems that you might like if... Read More

Questions & Answers

Q: What is the first recommended competition for beginners in computer vision?

The first recommended competition is the MNIST dataset for binary classification of dogs vs cats. It is a simple dataset to start with and offers opportunities to apply deep learning models for image classification.

Q: What is the challenge in the pneumonia detection competition?

The pneumonia detection competition involves working with DICOM medical images and building a model to classify whether an image shows signs of pneumonia or not. It offers the opportunity to learn about processing DICOM images and working with medical imaging data.

Q: What is the main objective of the right whale identification competition?

The right whale identification competition requires building a face recognition system for identifying right whales in aerial photographs. Participants have to create a bounding box around the whale's face and develop a model for accurate identification.

Q: What skill can be learned from the Carvana image masking challenge?

The Carvana image masking challenge focuses on image segmentation, where participants need to segment car images from the background. It is an opportunity to learn about different image segmentation techniques and models.

Q: How is the fashion image challenge different from other competitions?

The fashion image challenge involves segmenting fashion images into different clothing and accessory parts. It combines both image segmentation and object detection, offering beginners a chance to explore different models and techniques.

Q: What is the last competition recommended for beginners?

The last recommended competition is the generative dog images challenge, where participants need to generate realistic dog images using GANs. It allows beginners to learn about generative models and explore their creativity.

Summary & Key Takeaways

• The video presents a list of recommended computer vision competitions for beginners on Kaggle, starting with the simple MNIST dataset for binary classification of dogs vs cats.

• The list also includes multi-class flower classification, c discount image classification, pneumonia detection from medical images, facial keypoint detection, right whale identification, TGS salt identification, carvana image masking, fashion image segmentation, wheat head detection, and generative dog image generation using GANs.

• Each competition offers unique challenges and learning opportunities for beginners to develop computer vision skills.