A zoo for models tuned for OpenCV DNN with benchmarks on different platforms.
Guidelines:
-To clone this repo, please install [git-lfs](https://git-lfs.github.com/), run `git lfs install` and use `git lfs clone https://github.com/opencv/opencv_zoo`.
- To run benchmark on your hardware settings, please refer to [benchmark/README](./benchmark/README.md).
- Understand model filename: `<topic>_<model_name>_<dataset>_<arch>_<upload_time>`
-`<topic>`: research topics, such as `face detection` etc.
-`<model_name>`: exact model names.
-`<dataset>`: (Optional) the dataset that the model is trained with.
-`<arch>`: (Optional) the backbone architecture of the model.
-`<upload_time>`: the time when the model is uploaded, meaning the latest version of this model unless specified.
## Models & Benchmarks
| Model | Input Size | CPU x86_64 (ms) | CPU ARM (ms) | GPU CUDA (ms) |
- The time data that shown on the following table presents the time elapsed from preprocess (resize is excluded), to a forward pass of a network, and postprocess to get final results.
- The time data that shown on the following table is the median of 10 runs. Different metrics may be applied to some specific models.
- The data under each column of hardware setups on the above table represents the elapsed time of an inference (preprocess, forward and postprocess).
- The time data is the median of 10 runs after some warmup runs. Different metrics may be applied to some specific models.
- Batch size is 1 for all benchmark results.
-`---` represents the model is not availble to run on the device.
- View [benchmark/config](./benchmark/config) for more details on benchmarking different models.
-`---` means this model is not availble to run on the device.