From Data to Diamonds: Unearth the True Value of Quality Data

Webinar Recording Acquiring a dataset is just the beginning; the real challenge lies in refining it for training a Computer Vision model. Bloated, low-quality datasets waste resources and hamper model performance. The key to effective curation? Active Learning pipelines. By employing Active Learning, teams can intelligently select data that significantly impacts the model's performance. This method focuses on the model's current needs, ensuring each data point is impactful. The result is a streamlined annotation process and a more accurate, efficient Computer Vision model. Here are the key resources from the webinar: [Guide] How to curate your data [Case Study] See how one customer increased mAP by 20% through reducing their dataset size by 35% with visual data curation

Foundation models, like Meta’s Segment Anything Model (SAM), have provided a host of benefits for data and ML teams looking to expedite the production of training data whilst improving the quality. This webinar walks you through how to go one step further and fine-tune foundation models, in particular Meta AI's SAM, to maximize relevance to your specific use case Here are the key resources from the webinar: Encord Active GitHub - our open source tool that allowed us to conduct our research The Google Colab Notebook used in yesterday’s session Our ML Solutions Engineer’s Fine-Tune SAM blog post

The European Parliament recently voted to adopt the EU AI Act, marking the world’s first piece of legislation on artificial intelligence. The legislation intends to ban systems with an “unacceptable level of risk” and establish guardrails for developing and deploying AI systems into production. We've put together this learning pack to equip you with all the resources you need to understand how the AI Act will impact you, whether or not you're based in the EU. From webinar recordings to the most informative blogs, sign up now to get access to these crucial resources.