Test, download, or fine-tune open-source face detection datasets and models on Roboflow. Train a custom detector with RF-DETR, ship it with commercial-safe licensing, and keep face data on-device.
YOLO-Face is the Roboflow Universe collection of open-source face detection datasets and pre-trained models. Fork one, fine-tune it, and ship.
Browse the YOLO-Face collection and fork a face detection project into your workspace, or upload and label your own images in Annotate with AI-assisted labeling. A few hundred images covering your real lighting, angles, distances, and occlusion beats a huge but narrow set.
In Roboflow Train, we recommend RF-DETR, Roboflow's state-of-the-art real-time detection architecture. It leads current YOLO releases on accuracy and latency and ships under a commercial-friendly license. YOLO models are supported too if you specifically need them.
Check class-wise performance and test on images that look like your real deployment, not just clean samples. For faces, pay attention to performance across different lighting, angles, and demographics. A model that works on one population and not another is not ready.
Drawing a bounding box around every face in an image. It does not identify anyone. Detection is what powers the most common and least sensitive uses: privacy redaction, counting and occupancy, attribute analysis, and presence triggers, often in service of removing identity rather than capturing it.
Identity recognition adds a separate downstream step: the detector crops the face, then a face-embedding model turns that crop into an embedding and compares it against a gallery. That second step is where the real privacy and accuracy stakes live, and you should only add it if your use case genuinely calls for it.
Get reliable face detection first, and the downstream steps become tractable. Roboflow gives you the detection foundation, and the choice of what to build on top stays yours.
Commercial-safe by license, private by architecture, and backed by 40+ open datasets to start from.
Train and ship on RF-DETR, released under the permissive Apache 2.0 license, free to use commercially with no copyleft obligations. The Ultralytics YOLO family ships under AGPL-3.0, which in practice can require open-sourcing your application or buying a commercial license. Build a face detector you can actually ship.
Roboflow is a US-based platform with SOC 2 Type II compliance and encryption in transit and at rest. Deploy on-device, on-prem, in your VPC, or fully air-gapped, so face data never has to leave your infrastructure, which is often the right call for this use case.
The YOLO-Face collection on Roboflow Universe spans general human faces, expression detection, thermal and infrared faces, and more. Test any project in the browser, download it as a labeled dataset, or fine-tune it into your own model.
Half the Fortune 100 build computer vision with Roboflow, with detection models deployed in retail, transit, events, plants, and on the edge.
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YOLO-Face is a Roboflow Universe collection of open-source face detection datasets and pre-trained models, more than 40 community projects ranging from general human face detection to expression detection, thermal and infrared faces, and animal faces. Each one is testable in the browser, downloadable as a labeled dataset, and deployable via API. At its core, YOLO-Face does one job: drawing a bounding box around every face in an image, which is the foundation that blurring, counting, attribute analysis, and recognition are built on top of.
Start from a dataset by forking a project from the YOLO-Face collection on Universe, or upload and label your own images in Annotate with AI-assisted labeling. Then train RF-DETR, Roboflow's state-of-the-art real-time detection architecture, in Roboflow Train. Evaluate honestly on images that match your real deployment, paying attention to performance across lighting, angles, and demographics. Deploy with Inference on the cloud or the edge, and use active learning to fold uncertain frames back into the next version. You can go from a public dataset to a deployed face detector in an afternoon.
RF-DETR is released under the Apache 2.0 license, free to use commercially with no copyleft obligations, which is one reason it is the recommended model for a custom face detector you intend to ship. The Ultralytics YOLO family is distributed under AGPL-3.0, a strong copyleft license that in practice requires open-sourcing the application you build around the model or buying a commercial license, even for many commercial uses. If you build on a YOLO model, confirm the license before you ship.
Face detection finds where faces are; it does not identify anyone. Many of the most common uses, like privacy redaction, counting, and occupancy, use detection in service of removing or never capturing identity. Recognition is a separate downstream step: the detector crops the face, then a separate face-embedding model compares it against a gallery, and that second step is where the real privacy and accuracy stakes live. On-device deployment keeps face data local, which is often the right call, and models should be evaluated across different lighting, angles, and demographics before use.
Explore the YOLO-Face collection, fork a dataset, and fine-tune a custom detector you can ship.
Ask the Roboflow assistant about forking a dataset, training RF-DETR, and deploying on-device.
Test, download, or fork 40+ open face detection datasets and models on Universe.
See how to find faces in an image or video frame and build on top of it.
Understand the copyleft obligations before you ship a YOLO-based model.
