[notebooks](https://github.com/roboflow/notebooks) | [supervision](https://github.com/roboflow/supervision) | [autodistill](https://github.com/autodistill/autodistill) | [maestro](https://github.com/roboflow/multimodal-maestro)
[](https://badge.fury.io/py/inference) [](https://pypistats.org/packages/inference)  [](https://github.com/roboflow/inference/blob/main/LICENSE.core) [](https://huggingface.co/spaces/Roboflow/workflows) [](https://discord.gg/GbfgXGJ8Bk)
👋 hello
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, active learning, and commercial licenses for YOLOv5 and YOLOv8.
💻 install
Inference package requires Python>=3.8,<=3.11. Click here to learn more about running Inference inside Docker.
pip install inference👉 additional considerations
- hardware Enhance model performance in GPU-accelerated environments by installing CUDA-compatible dependencies. ```bash pip install inference-gpu ``` - models The `inference` and `inference-gpu` packages install only the minimal shared dependencies. Install model-specific dependencies to ensure code compatibility and license compliance. Learn more about the [models](https://inference.roboflow.com/#extras) supported by Inference. ```bash pip install inference[yolo-world] ```🔥 quickstart
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")👉 roboflow models
Set up your `ROBOFLOW_API_KEY` to access thousands of fine-tuned models shared by the [Roboflow Universe](https://universe.roboflow.com/) community and your custom model. Navigate to 🔑 keys section to learn more. ```python 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 ) ```
👉 foundational models
- [CLIP Embeddings](https://inference.roboflow.com/foundation/clip) - generate text and image embeddings that you can use for zero-shot classification or assessing image similarity. ```python from inference.models import Clip model = Clip() embeddings_text = clip.embed_text("a football match") embeddings_image = model.embed_image("https://media.roboflow.com/inference/soccer.jpg") ``` - [Segment Anything](https://inference.roboflow.com/foundation/sam) - segment all objects visible in the image or only those associated with selected points or boxes. ```python from inference.models import SegmentAnything model = SegmentAnything() result = model.segment_image("https://media.roboflow.com/inference/soccer.jpg") ``` - [YOLO-World](https://inference.roboflow.com/foundation/yolo_world) - an almost real-time zero-shot detector that enables the detection of any objects without any training. ```python from inference.models import YOLOWorld model = YOLOWorld(model_id="yolo_world/l") result = model.infer( image="https://media.roboflow.com/inference/dog.jpeg", text=["person", "backpack", "dog", "eye", "nose", "ear", "tongue"], confidence=0.03 ) ```📟 inference server
-
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 aROBOFLOW_API_KEY. For further information, visit the 🔑 keys section.
🎥 inference pipeline
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()🔑 keys
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>📚 documentation
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.
⚡️ Model-specific extras
Explore the list of inference extras to install model-specific dependencies.
© license
See the "Self Hosting and Edge Deployment" section of the Roboflow Licensing documentation for information on how Roboflow Inference is licensed.
🏆 contribution
We would love your input to improve Roboflow Inference! Please see our contributing guide to get started. Thank you to all of our contributors! 🙏
