Run Moondream Tiny Vision Language Model Locally on CPU - Object Detection and Image Understanding

Описание: #moondream #visionmodel #computervision #llm #machinelearning #llm #pytorch
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In this tutorial, we explain how to install and run locally a tiny vision language model called Moondream. This is a very small (0.5 B and 2B) vision language model that can be executed both on CPUs and GPUs.

The model is versatile and can be used for describing images, object detection, pointing, captioning, etc. The main advantage of this model is that it has a very small size (0.5B) and can be executed on CPUs. As such, it is ideal for edge devices. Of course, the model speed of inference can be accelerated by using GPUs.

In this video tutorial, we explain how to install and run a CPU-only version of Moondream. Our computer has an Intel i9 processor with 48GB RAM. In the next tutorial, we will try to run Moondream on Raspberry Pi 5.

A lot of viewers of this channel are complete beginners or know very little about vision language models. Consequently, let us explain the main idea.

-A user provides an image and a question as inputs to the model. For example, we can provide an image and ask the model to describe what is on the image. The vision language model analyzes and “understands” what is in the image and provides the answer in the written form. This is just one example of capabilities of vision language models. Vision language models can also be used for complex reasoning and object detection.

In the future, vision language models will serve as the backbone of robotics systems. For example, image an elderly person who gives voice commands to a humanoid robot. For example, give me a yellow book standing on the middle shelf in the corner of the room. The robot equipped with a camera will take a photo of the room and will use a vision language model to perform object detection and retrieve the book.