With no prior knowledge of machine learning or device-specific deployment, you can deploy a computer vision model to a range of devices and environments using Roboflow Inference CLI.
Project description
Roboflow Inference CLI
Roboflow Inference is an opinionated tool for running inference on state-of-the-art computer vision models. With no prior knowledge of machine learning or device-specific deployment, you can deploy a computer vision model to a range of devices and environments. Inference supports object detection, classification, and instance segmentation models, and running foundation models (CLIP and SAM).
🎥 Inference in action
Check out Inference running on a video of a football game:
https://github.com/roboflow/inference/assets/37276661/121ab5f4-5970-4e78-8052-4b40f2eec173
👩🏫 Examples
The /examples directory contains example code for working with and extending inference, including HTTP and UDP client code and an insights dashboard, along with community examples (PRs welcome)!
inference serve
inference serve is the main command for starting a local inference server. It takes a port number and will only start the docker container if there is not already a container running on that port.
inference serve --port 9001
inference infer
inference infer is the main command for running inference on a single image. It takes a path to an image, a Roboflow project name, model version, and API key, and will return a JSON object with the model's predictions. You can also specify a host to run inference on our hosted inference server.
Local image
inference infer --image ./image.jpg --project_id my-project --model-version 1 --api-key my-api-key
Hosted image
inference infer --image https://[your-hosted-image-url] --project_id my-project --model-version 1 --api-key my-api-key
Hosted inference
inference infer --image ./image.jpg --project_id my-project --model-version 1 --api-key my-api-key --host https://infer.roboflow.com
💻 Why Inference?
Inference provides a scalable method through which you can manage inferences for your vision projects.
Inference is backed by:
-
A server, so you don’t have to reimplement things like image processing and prediction visualization on every project.
-
Standardized APIs for computer vision tasks, so switching out the model weights and architecture can be done independently of your application code.
-
Model architecture implementations, which implement the tensor parsing glue between images and predictions for supervised models that you've fine-tuned to perform custom tasks.
-
A model registry, so your code can be independent from your model weights & you don't have to re-build and re-deploy every time you want to iterate on your model weights.
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Data management integrations, so you can collect more images of edge cases to improve your dataset & model the more it sees in the wild.
And more!
📝 license
The Roboflow Inference code is distributed under an Apache 2.0 license. The models supported by Roboflow Inference have their own licenses. View the licenses for supported models below.
| model | license |
|---|---|
inference/models/clip |
MIT |
inference/models/gaze |
MIT, Apache 2.0 |
inference/models/sam |
Apache 2.0 |
inference/models/vit |
Apache 2.0 |
inference/models/yolact |
MIT |
inference/models/yolov5 |
AGPL-3.0 |
inference/models/yolov7 |
GPL-3.0 |
inference/models/yolov8 |
AGPL-3.0 |
🚀 enterprise
With a Roboflow Inference Enterprise License, you can access additional Inference features, including:
- Server cluster deployment
- Device management
- Active learning
- YOLOv5 and YOLOv8 model sub-license
To learn more, contact the Roboflow team.
📚 documentation
Visit our documentation for usage examples and reference for Roboflow Inference.
🏆 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! 🙏
💻 explore more Roboflow open source projects
| Project | Description |
|---|---|
| supervision | General-purpose utilities for use in computer vision projects, from predictions filtering and display to object tracking to model evaluation. |
| Autodistill | Automatically label images for use in training computer vision models. |
| Inference (this project) | An easy-to-use, production-ready inference server for computer vision supporting deployment of many popular model architectures and fine-tuned models. |
| Notebooks | Tutorials for computer vision tasks, from training state-of-the-art models to tracking objects to counting objects in a zone. |
| Collect | Automated, intelligent data collection powered by CLIP. |
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