Inference turns any computer or edge device into a command center for your computer vision projects.
- 🛠️ Self-host your own fine-tuned models
- 🧠 Access the latest and greatest foundation models (like Florence-2, CLIP, and SAM2)
- 🤝 Use Workflows to track, count, time, measure, and visualize
- 👁️ Combine ML with traditional CV methods (like OCR, Barcode Reading, QR, and template matching)
- 📈 Monitor, record, and analyze predictions
- 🎥 Manage cameras and video streams
- 📬 Send notifications when events happen
- 🛜 Connect with external systems and APIs
- 🔗 Extend with your own code and models
- 🚀 Deploy production systems at scale
See Example Workflows for common use-cases like detecting small objects with SAHI, multi-model consensus, active learning, reading license plates, blurring faces, background removal, and more.
time-in-zone.mp4
Install Docker (and NVIDIA Container Toolkit for GPU acceleration if you have a CUDA-enabled GPU). Then run
pip install inference-cli && inference server start --dev
This will pull the proper image for your machine and start it in development mode.
In development mode, a Jupyter notebook server with a quickstart guide runs on http://localhost:9001/notebook/start. Dive in there for a whirlwind tour of your new Inference Server's functionality!
Now you're ready to connect your camera streams and start building & deploying Workflows in the UI or interacting with your new server via its API.
A key component of Inference is Workflows, composable blocks of common functionality that give models a common interface to make chaining and experimentation easy.
With Workflows, you can:
- Detect, classify, and segment objects in images using state-of-the-art models.
- Use Large Multimodal Models (LMMs) to make determinations at any stage in a workflow.
- Seamlessly swap out models for a given task.