Spring is finally here and there is computer vision in the air! 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 and Annotate
- Launched Polygonal Annotation tool
- Launched Multi-label Classification import and labeling
- UX enhancements when creating a workspace
- Mobile responsiveness updates
- Support binary file uploads in the upload API
- Added user moderation tool
- Added support for importing Sagemaker Ground Truth annotation files on all plans
- Added "filter by class" view for Classification projects (click the class name from Health Check)
Roboflow Train and Deploy
- Launched Multi-label Classification Training
- Launched Multi-label Classification Hosted Inference API
- Inference APIs now support multipart requests
- Updated webcam demo with per-class confidence thresholds, camera flip option, and custom code integration
- Support for GPU inference server offline mode when network connection is completely disconnected
Roboflow Universe
- Added 5 Popular Industry pages (Self Driving, Manufacturing, Gaming, Agriculture, Sports)
- Inference test widget now supports Single-Class and Multi-Class Classification projects
- SEO page improvements
- Mobile responsiveness updates
- Overall loading and speed improvements
Other
- Product team finalized roadmap for Q2
- Team meetups in Des Moines, Salt Lake City and Austin
- Roboflow hosted a recruiting event in Des Moines
- Visited a new customer's facility
- Updated core analytics tool to PostHog
- Added 2 new team members.
- Opened several positions (come help us democratize computer vision!)
- 26 bug fixes
- 36 discussions on the Roboflow Forum
- 6 new Universe projects
- 8 new blog posts
- 4 new YouTube videos
- Featured in/by: TechCrunch, Ampera Racing, Nakashima (JP ENG), Queen's AutoDrive, AI for Wildlife.
- User projects: OAK + Roboflow Demo, SHEL5K: An Extended Dataset and Benchmarking for Safety Helmet Detection, Animal Behaviour and Speech Recognition, YOLOv5 Honey Bee Detection Model, Detection of Plant Diseases Using Leaf Images and Machine Learning, Identifying Fish Species, Fruit detection dataset, A Video Analytics System for Person Detection Combined with Edge Computing, Deep learning techniques to classify agricultural crops through UAV imagery: a review, IIOT Course, A visual approach towards forward collision warning for autonomous vehicles on Malaysian public roads, Deep learning techniques to classify agricultural crops through UAV imagery, Deep-Learning-Based Object Filtering According to Altitude for Improvement of Obstacle Recognition during Autonomous Flight, The practical guide for Object Detection with YOLOv5 algorithm, A Large-scale Dataset for Fine-grained and Domain-adaptive Recognition of Cereal Grains, Fast and Accurate Face Detector, Autonomous Mosquito Habitat Detection Using Satellite Imagery and Convolutional Neural Networks For Disease Risk Mapping, Development of an Aerial Fire Identification System Based on Visual Artificial Intelligence, Vehicle Detection for Vision-Based Intelligent Transportation Systems Using Convolutional Neural Network Algorithm, Trust-driven Reinforcement Selection Strategy for Federated Learning on IoT Devices