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Basic Information

Roboflow Inference is a self-hostable computer vision server and toolkit that turns a computer or edge device into a command center for computer vision projects. It provides a local or cloud-connected inference server, an SDK and REST API, and a Jupyter-based quickstart to run models and orchestrate Workflows. The repository supports running pre-trained and fine-tuned models as well as foundation models such as Florence-2, CLIP, and SAM2. It is designed to manage cameras and video streams, perform image and video processing on-device or in hosted environments, and integrate with Roboflow cloud features when an API key is provided. The project includes documentation, examples, tutorials, and enterprise components for larger deployments. The core is Apache 2.0 licensed and enterprise extensions and model licensing details are provided within the repo and Roboflow’s docs.

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App URL
https://github.com/roboflow/inference
Github Repository
https://github.com/roboflow/inference/blob/main/README.md

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App Details

Features
Workflows: composable blocks that chain models, add business logic, and extend functionality with custom Python blocks. Multi-model support: run state-of-the-art object detection, classification, and segmentation models and swap models for tasks. Video pipeline capabilities: connect RTSP streams, webcams, and handle hardware acceleration, multiprocessing, decoding, and GPU batching. APIs and SDKs: a Python SDK and OpenAPI/Redoc REST endpoints to start pipelines, consume results, and manage servers. Notifications and integrations: built-in notification blocks such as Email, Twilio, and Webhooks to call external services. Deployment options: self-host on diverse hardware including Raspberry Pi and NVIDIA Jetson or use Roboflow hosted compute and dedicated deployments. Monitoring and device management features are available for cloud-linked accounts.
Use Cases
Inference helps teams build production computer vision systems faster by providing an integrated server, orchestration, and tooling for both edge and cloud deployment. It lets developers prototype with a local Jupyter quickstart, run and compare models via Workflows, and operate continuous video pipelines that detect, count, time, and visualize objects. The platform supports common use-cases shown in examples and tutorials such as license plate OCR, smart parking, self-checkout, and active learning workflows. Organizations gain flexibility to self-host for privacy and low-latency needs or use hosted compute for simplified operations. Enterprise options, device management, model monitoring, and extensibility with custom code blocks simplify scaling, integration with external systems, and maintaining deployed CV services.

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