Minimal HW Requirements
CPU- 2.4GHz x86 processor (single core per feed)
PoE IP Cameras- Minimum 720p resolution and 7 FPS- MJPEG or RTSP Stream format
Internet Connectivity for StatisticsAny ethernet router capable of sending a ~50MB of data/month
9 research papers
3 patents pending
6x faster inference
6x faster to implement
16x smaller footprint
3.75x less energy
70% accuracy improvement
Smaller models, smaller machines and smaller costs:Thanks to our optimisations, deploy Datakalab on machines that are cheaper than GPUs and still readily available despite supply chain constraints.
Algorithms that dynamically adjust to changing lighting conditions automagically: It's not the same thing detecting objects at dawn than it is at noon or evening. Datakalab algorithms self adapt during the day to maintain optimal precision even when the lighting conditions change or are different between cameras.
Compression that saves bandwidth: We remove the unnecessary weights in the models so that they can rapidly be updated and deployed without costing a fortune in bandwidth or compute.
Re-use existing cameras and hardware: You don't have to go out and get a new set of cameras that are stuck doing only one thing. Take advantage of your existing vision infrastructure and add counting, demographics or other use cases without having to add or cable additional hardware.