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Roboflow Inference is an open-source platform designed to simplify the deployment of computer vision models. It enables developers to perform object detection, classification, and instance segmentation and utilize foundation models like CLIP, Segment Anything, and YOLO-World through a Python-native package, a self-hosted inference server, or a fully managed API.

Explore our enterprise options for advanced features like server deployment, device management, active learning, and commercial licenses for YOLOv5 and YOLOv8.

Inference package requires Python>=3.8,<=3.11. Click here to learn more about running Inference inside Docker.

pip install inference

Use Inference SDK to run models locally with just a few lines of code. The image input can be a URL, a numpy array (BGR), or a PIL image.

from inference import get_model

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

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

from inference import get_model

model = get_model(model_id="soccer-players-5fuqs/1")

results = model.infer(
    image="https://media.roboflow.com/inference/soccer.jpg",
    confidence=0.5,
    iou_threshold=0.5
)

• deploy server

  The inference server is distributed via Docker. Behind the scenes, inference will download and run the image that is appropriate for your hardware. Here, you can learn more about the supported images.

  inference server start

• run client

  Consume inference server predictions using the HTTP client available in the Inference SDK.

  from inference_sdk import InferenceHTTPClient
  
  client = InferenceHTTPClient(
      api_url="http://localhost:9001",
      api_key=<ROBOFLOW_API_KEY>
  )
  with client.use_model(model_id="soccer-players-5fuqs/1"):
      predictions = client.infer("https://media.roboflow.com/inference/soccer.jpg")

  If you're using the hosted API, change the local API URL to https://detect.roboflow.com. Accessing the hosted inference server and/or using any of the fine-tuned models require a ROBOFLOW_API_KEY. For further information, visit the 🔑 keys section.

The inference pipeline is an efficient method for processing static video files and streams. Select a model, define the video source, and set a callback action. You can choose from predefined callbacks that allow you to display results on the screen or save them to a file.

from inference import InferencePipeline
from inference.core.interfaces.stream.sinks import render_boxes

pipeline = InferencePipeline.init(
    model_id="yolov8x-1280",
    video_reference="https://media.roboflow.com/inference/people-walking.mp4",
    on_prediction=render_boxes
)

pipeline.start()
pipeline.join()

Inference enables the deployment of a wide range of pre-trained and foundational models without an API key. To access thousands of fine-tuned models shared by the Roboflow Universe community, configure your API key.

export ROBOFLOW_API_KEY=<YOUR_API_KEY>

Visit our documentation to explore comprehensive guides, detailed API references, and a wide array of tutorials designed to help you harness the full potential of the Inference package.

The Roboflow Inference code is distributed under the Apache 2.0 license. However, each supported model is subject to its licensing. Detailed information on each model's license can be found here.

We would love your input to improve Roboflow Inference! Please see our contributing guide to get started. Thank you to all of our contributors! 🙏