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As of February 05, 7:42 PM +08
1/ We are thrilled to announce an early access feature to #Nexus - Video Labelling! 🎉
Read the Announcement 👉 https://t.co/0TgJZCCs8f https://t.co/GcdneW6zEr
1/🚀We are excited to showcase how students at @sgSMU have recently used our Datature #Nexus platform to create a range of innovative computer vision projects without any code!
Check out these 3 particularly promising applications 👉🏼https://t.co/6Oa6oHXAmV
1/ Are you looking for an easy way to create an inference dashboard for your machine-learning model? 🤖
#Streamlit is a powerful and easy-to-use open-source app framework that allows you to easily convert your data scripts into informative, clutter-free web applications.
🤖 By leveraging the power of #RaspberryPi and state-of-the-art #machinelearning algorithms, we can create efficient and reliable vision systems that can be deployed on the edge.
Set up Datature Edge on a Raspberry Pi 👉🏼https://t.co/PhF8GnQ3QD
1/ #TFLite is a powerful tool for machine learning, allowing for the creation of efficient and lightweight models that can be easily deployed on mobile 📱 and edge devices.
1/ In this article, we discuss how to run a custom #TensorFlow.js Object Detection Model in Node-RED 🚀 This allows for the integration of #machinelearning capabilities into your #NodeRED flow, enabling real-time object detection in your #IoT applications 💻
1/ 🚀 Datature was super stocked to have an in-depth sharing session with @CapgeminiInvent and to be sharing how Datature is powering breakthrough AI and enabling hundreds of enterprises.
Check out this comprehensive tutorial (code included!) written by @AnelloEugenia on how you can build your very own deep-learning app on Datature with your own dataset swiftly ⚡️ https://t.co/tjrJTjtRN7
Check out this in-depth article on how to build a breast lesion detection pipeline with #deeplearning and web application using open-source #datasets and Streamlit 👉🏼
1/ 👀 Observing data distributions (for spatial and annotations) is a very important aspect of #deeplearning because it can have a large impact on your model performance and robustness to real-world variations.