inference - Simplify Deployment of Computer Vision Models with the Inference Platform

Introduction to Roboflow Inference

Roboflow Inference is a robust open-source platform crafted to make the deployment of computer vision models both straightforward and accessible. This platform caters to developers aiming to tackle tasks such as object detection, classification, and instance segmentation. Moreover, it supports the use of foundational models like CLIP, Segment Anything, and YOLO-World through various means such as a Python-native package, a self-hosted inference server, or a fully managed API.

Key Features

Popular Models Access: Roboflow Inference provides seamless integration with popular models like CLIP, which generates text and image embeddings, as well as Segment Anything that segments objects in images, and YOLO-World for zero-shot detection of objects without prior training.
Simplified Deployment: It offers various avenues for model deployment that include locally using their SDK, server deployment for more scalable solutions, or utilizing a managed API for simplified use cases.
Enterprise Options: Advanced features like server deployment, active learning, and commercial licenses for industry-recognized models such as YOLOv5 and YOLOv8 are available for businesses looking for enhanced solutions.

Installation and Requirements

To get started with Roboflow Inference, Python versions between 3.8 and 3.11 are required. Installation is simple with pip:

pip install inference

For GPU-accelerated environments, additional CUDA-compatible dependencies can be installed to boost model performance:

pip install inference-gpu

Using the Inference SDK

The Inference SDK allows for the running of models locally with ease. The input images can be in various formats, such as URLs, numpy arrays, or PIL images. Here's a simple example:

from inference import get_model

model = get_model(model_id="yolov8n-640")

results = model.infer("https://media.roboflow.com/inference/people-walking.jpg")

Inference Server and Pipelines

For those interested in deploying an inference server, Docker can be used to distribute the server easily, facilitating seamless server setup for predictions:

inference server start

Inference pipelines present an efficient way to handle static video files and streams, allowing for model selection, video source definition, and setting callback actions to process video data effectively.

Accessing Models

The Roboflow Universe and foundational models like CLIP, Segment Anything, and YOLO-World are accessible within this platform. Users can configure their API key to access thousands of fine-tuned models shared by the community.

export ROBOFLOW_API_KEY=<YOUR_API_KEY>

Documentation and Community

Roboflow provides comprehensive documentation to guide users through the platform functionalities and use cases. Community and developer contributions are encouraged to further enhance the platform's capabilities.

Explore Roboflow Inference to leverage sophisticated computer vision technologies, simplify deployments, and achieve outstanding outcomes in visual data processing. Whether a seasoned developer or entering the realm of AI, Roboflow Inference presents tools and solutions tailored to diverse needs.