1.Computer Vision Roadmap

Computer Vision Roadmap that I have been working on for a while. It is a collection of resources that I have found useful in my journey to learn Computer Vision. I hope it will be useful for you as well.

The scope of computer vision is growing fast. According to a report, the market for computer vision is expected to increase from US$10.9 billion in 2019 to US$17.4 billion by 2024, at a growing CAGR of 7.8%.

So, to learn Computer Vision, you should have the following skills-

• Mathematics - Linear Algebra, Calculus, Probability, Statistics
• Programming - Python, C/C++, Java
• OpenCV - OpenCV is a library of programming functions mainly aimed at real-time computer vision. It is used to process images and videos to identify objects, faces, or even the handwriting of a human.
• Deep Learning Frameworks - TensorFlow, PyTorch, Keras, Caffe, Caffe2, Theano, Torch, MxNet, Chainer, Deeplearning4j, PaddlePaddle, etc.
• Convolutional Neural Networks - CNNs are a type of neural network that is mostly used in image recognition and classification tasks. CNNs are used to extract features from images and then classify them.
• Recurrent Neural Networks - RNNs are a type of neural network that is mostly used in natural language processing tasks. RNNs are used to extract features from text and then classify them.
• Practice with real-world datasets: Work with real-world datasets such as ImageNet, COCO, and Pascal VOC to gain experience with different computer vision tasks.
• Attend workshops and conferences: Attend computer vision workshops and conferences to stay up-to-date with the latest developments in the field and network with other computer vision engineers.
• Build your portfolio: Create a portfolio of computer vision projects that demonstrate your skills and experience. This can include projects such as object detection, image classification, and semantic segmentation.
• Continuously learn and improve: Keep learning and improving your skills by staying updated with the latest trends and technologies in computer vision. Attend workshops, take online courses, and read research papers to stay at the forefront of the field.