deep-learning

This organization contains software for realtime computer vision published by the members, partners and friends of the BMW TechOffice MUNICH and InnovationLab. Link

Streamlit enables data scientists and machine learning practitioners to build data and machine learning applications quickly. In this piece, we will look at how we can use Streamlit to build a face verification application. However, before we can start verifying faces, we have to detect them. In computer vision, face detection is the task of locating and localizing faces in an image. Face verification is the process of comparing the similarity of two or more images.

Slack chats can become messy with time, proving difficult to extract meaningful information. In this article, I want to present a quick codeless way of fine-tuning and deploying the commonly used BERT classifier to do conversational analysis. We will use that system to extract tasks, facts, and other valuable information from our Slack conversations. It could be easily extended for categorizing any other textual data, like support requests, emails, etc.

This project demonstrates the Udacity self-driving-car dataset and YOLO object detection into an interactive Streamlit app. The complete demo is implemented in less than 300 lines of Python and illustrates all the major building blocks of Streamlit. Link

Introduced in the paper, An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, Vision Transformers (ViT) are the new talk of the town for SOTA image classification. Experts feel this is only the tip of the iceberg when it comes to Transformer architectures replacing their convolutional counterparts for upstream/downstream tasks. Link