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

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December 17, 2020
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Abhishek Thakur
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Best computer vision competitions on Kaggle (for beginners)

TL;DR

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

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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.

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