You can build a project to detect certain types of shapes.
"Colorful Image Colorization." If you are looking for the implementation of the project, I will suggest you look at the following article: Also, I suggest you go through this prominent paper on Image Captioning. MediaPipe is the simplest way for researchers and developers to build world-class ML solutions and applications for mobile, edge, cloud and the web. We build a fully integrated software for 3D reconstruction, photomodeling and camera tracking.
Helping you create AIs / Bots that learn to play any game you own! Face Recognition – GitHub Link 1, GitHub Link 2, Video Tutorial. Diversify your portfolio by working on the following open-sourced datasets for object detection: Open Image is a dataset of ~9M images annotated with image-level labels, object bounding boxes, object segmentation masks, visual relationships, and localized narratives.
Full code for Face and Eyes Detection using Haar Cascades: 2. Deep Learning toolkit for Computer Vision. Semantic Segmentation: Introduction to the Deep Learning Technique Behind Google Pixel’s Camera!
To better understand the development in face recognition technology in the last 30 years, I’d encourage you to read an interesting paper titled: Neural style transfer is a computer vision technology that recreates the content of one image in the style of the other image.
Open Source Differentiable Computer Vision Library for PyTorch, Pure Go face detection, pupil/eyes localization and facial landmark points detection library, A curated list of awesome self-supervised methods, [CVPR2019] Fast Online Object Tracking and Segmentation: A Unifying Approach. This project was built using dlib state-of-the-art facial recognition library which is built on top of deep learning. They are very important in recognizing a person’s emotions. For text detection, I found a state of the art deep learning method EAST (Efficient Accurate Scene Text Detector). Feature Extraction: Later, features are extracted that can be used in the recognition task. Also, here I am listing down some useful CV resources to help you explore the deep learning and Computer vision world: Convolutional Neural Networks (CNN) from Scratch (Free). Each of these video clips contains 20 frames with an annotated last frame. It streamlines the training pipeline by viewing object detection as a direct set prediction problem.
MediaPipe is the simplest way for researchers and developers to build world-class ML solutions and applications for mobile, edge, cloud and the web. Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation, DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs, Hands-On Tutorial on Real-Time Lane Detection using OpenCV (Self-Driving Car Project! They seriously have some exciting courses. Papers by organizations sharing their work on applied data science & machine learning.
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