DeepFace vs MediaPipe
DeepFace
github.comMediaPipe
developers.google.comSide by side
| Figure | DeepFace | MediaPipe |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | Yes | — |
| Open source | Yes | Yes |
| Web | Yes | Yes |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| iOS | — | Yes |
| Android | — | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 6 | 2 |
| Spec score · AI Image Recognition Software | 8.8 (#4) | 8.6 (#8) |
| Documentation depth | Independent documentation indexes installation, quick start, core APIs, database integrations, models, REST deployment, Docker, benchmarking and testing. | The official site provides task guides, platform setup guides, API references, examples, framework concepts and build-from-source material. |
| What it does | DeepFace is a Python framework for face recognition and facial attribute analysis covering age, gender, emotion and race. | MediaPipe supplies customizable machine-learning libraries and tools for deploying AI features in applications across development platforms. |
Plans
DeepFace
- Anonymous trialFree
- deepface.dev free accessFree
- deepface.dev free allowanceFree
- DeepFace open-source packageFree
- Open-source DeepFaceFree
- deepface.dev EnterprisePriced on request
In their own words
MediaPipe
MediaPipe gives developers libraries, tools, ready-to-run models and customizable APIs for adding AI and machine learning to applications. Its vision tasks include face and hand landmarks, gesture recognition, image classification and segmentation, object detection, and pose landmarks. Text tasks cover language detection, classification, embeddings, proofreading and summarization, with audio classification also available. MediaPipe Tasks supports Android, Web/JavaScript, Python and iOS; its cross-platform APIs are described as requiring as few as five lines of code for inference. The optimized on-device pipelines use CPU, GPU and TPU acceleration for real-time use cases. Developers can try web samples in MediaPipe Studio with text, webcam input or uploaded images. Model Maker supports transfer learning for compatible models, but it is deprecated and cannot change a model's original task. Solution API inputs are processed on-device and not sent to Google; usage and performance metrics are sent to Google. The framework can be built from source for Linux, macOS, Windows and Docker, with some configurations marked experimental.