Raspberry Pi 4
A low-cost single-board computer equipped with a quad-core ARM Cortex-A72 CPU and a VideoCore VI GPU, commonly used in embedded and edge computing projects.
Timeline (last 90 days)
The Raspberry Pi 4 is equipped with a quad-core Cortex-A72 (clocked at about 1.5–1.8 GHz), typically 2/4/8 GB of memory, and a VideoCore VI GPU that supports only OpenGL ES 3.x and has no CUDA or general-purpose matrix acceleration units.
The CPU compute of the Raspberry Pi 4 is roughly on the order of single-digit to low-teens GFLOPS, two to three orders of magnitude behind desktop GPUs.
A Transformer with tens of millions of parameters often requires hundreds of milliseconds to several seconds for single-frame inference on a Raspberry Pi 4.
Running the full Depth Anything V2 model directly on a Raspberry Pi 4 would very likely result in a frame rate too low to meet real-time spraying requirements, and latency would become a critical flaw in the dual-model pipeline.
Cascading DINO with Depth Anything V2 and deploying them on a Raspberry Pi 4 is technically feasible in principle, but direct deployment would face multiple challenges in compute, memory, real-time performance, and accuracy.
All Facts (5)
The Raspberry Pi 4 is equipped with a quad-core Cortex-A72 (clocked at about 1.5–1.8 GHz), typically 2/4/8 GB of memory, and a VideoCore VI GPU that supports only OpenGL ES 3.x and has no CUDA or general-purpose matrix acceleration units.
50%UnverifiedThe CPU compute of the Raspberry Pi 4 is roughly on the order of single-digit to low-teens GFLOPS, two to three orders of magnitude behind desktop GPUs.
50%UnverifiedA Transformer with tens of millions of parameters often requires hundreds of milliseconds to several seconds for single-frame inference on a Raspberry Pi 4.
50%UnverifiedRunning the full Depth Anything V2 model directly on a Raspberry Pi 4 would very likely result in a frame rate too low to meet real-time spraying requirements, and latency would become a critical flaw in the dual-model pipeline.
50%UnverifiedCascading DINO with Depth Anything V2 and deploying them on a Raspberry Pi 4 is technically feasible in principle, but direct deployment would face multiple challenges in compute, memory, real-time performance, and accuracy.
50%