Part 1:

1. 1Explore the history of image classification models, starting from simple fish classification tasks
2. 2Run experiments using traditional convolutional neural networks (CNNs) to understand their limitations
3. 3Apply transformer architectures to image recognition tasks to leverage their ability to handle sequential data
4. 4Configure vision transformers to improve performance on specific image classification tasks
5. 5Test the robustness of vision transformers against various types of image distortions and noises

Data scientists, AI engineers, and computer vision specialists can benefit from understanding the progression of image recognition techniques to improve their models and applications

💡 Vision transformers have revolutionized image recognition by enabling machines to learn from sequential data, outperforming traditional CNNs in many tasks

🔍 From fish classification to vision transformers: uncovering the evolution of machine vision #AI #ComputerVision #MachineLearning