Caffe vs TensorFlow
Caffe
caffe.berkeleyvision.orgTensorFlow
tensorflow.orgSide by side
| Figure | Caffe | TensorFlow |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | — | Yes |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| iOS | — | Yes |
| Android | — | Yes |
| API | — | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 1 |
| Spec score · Deep Learning Software | 8.1 (#9) | 7.5 (#22) |
| Support | The site directs usage and installation questions to the caffe-users group and bug reports to GitHub Issues. | TensorFlow directs users to its issue tracker, release notes, Stack Overflow, community forum, and announcement mailing list. |
Plans
In their own words
Caffe
Caffe has no write-up on Specifiction yet; its figures are above.
TensorFlow
TensorFlow is a free, open-source platform for creating machine learning models and running them in different environments. Model-building options include the high-level Keras API, eager execution, and distributed training tools. Models can be deployed on servers, edge devices, and the web: TFX supports production pipelines, TensorFlow Lite supports mobile and edge inference, and TensorFlow.js covers JavaScript environments. The ecosystem also includes LiteRT, tf.data, TensorFlow Datasets, and TensorBoard. TensorFlow provides resources addressing fairness, interpretability, privacy, and security in machine learning workflows, including TF Privacy for privacy-focused training and TF Federated for federated learning. Supported 64-bit environments include Ubuntu, Windows, and macOS; WSL2 GPU support is marked experimental. TensorFlow's install guide states that macOS has no GPU support. Google Colab can run TensorFlow tutorials in browser-based Jupyter notebooks without installation or setup. The API and reference implementation were released under the Apache 2.0 license in November 2015. Listed platforms include Android, iOS, Linux, macOS, self-hosted, web, and Windows.