Roboflow Changelog: February 2022

Even though it's the shortest month of the year, we accomplished a lot! Each month, we share a recap of product and company updates; if you missed last month's edition of the Roboflow Changelog, you can find it here.

Roboflow Organize

• Polished and improved the new collaborative annotation workflow
• Improvements to tutorial & invitation flow
• Inference Widget is now available to use on trained versions of your datasets
• Enabled annotation via API to work with new annotation flow

Roboflow Train and Deploy

• Released an improved model for Roboflow Train
• You can now select whether you want to optimize for Speed or Accuracy when training
• Our REST API & PIP package can now access public projects from Universe
• Improved Server Benchmark repo to use the API for dataset loading and calculate mAP

Roboflow Universe

• Enhanced loading speed
• Improved auth and inference flow for logged out users
• Added a form to request specific projects be created or added

Other

• Launched the Roboflow Forum (join for great Roboflow and computer vision discussion daily!)
• Product team traveled to Minneapolis to do Q2 planning.
• Sales team had an on-site in Salt Lake City for training.
• Attended ProductCon.
• Added 2 new team members.
• Opened several positions in marketing and engineering (come help us democratize computer vision!)
• Improved our internal process for reporting and communicating bugs
• 28 bug fixes
• 4 new Universe projects
• 6 blog posts
• 1 YouTube video
• Featured in/by: YC Top Companies, Science Direct, Martian Chronicles, Tweet from CPO of Adobe, Nakashima, Mohammed Ismail
• User projects: Counting Teslas, Thermal Images, Detecting and Counting Bacteria, Deep Learn Me, Deep Learning-Based Object Detection and Scene Perception under Bad Weather Conditions, Suspicious Object Detection in Environments with Tear Gas, Computer Vision with Deep Convolutional Neural Network Approach for Cold-Flow Casting Defect Detection, A Pipeline Approach to Context-Aware Handwritten Text Recognition, A Novel Road Maintenance Prioritization System Based on Computer Vision and Crowdsourced Reporting, DRONET: Multi-Tasking Framework for Real-Time Industrial Facility Aerial Surveillance and Safety, Localization and Edge-Based Segmentation of Lumbar Spine Vertebrae to Identify the Deformities Using Deep Learning Models, Object Detection on Custom Dataset in 3 Simple Steps Using YOLOv5