Pre-trained Models

We provide some models for Core ML and ML modules.

  • If you installed Metavision SDK with the packages/installer, the link to download those models was shared during the sign-up (if you are a Prophesee customer, retrieve the link to the model by accessing to your SDK version in the Knowledge Center Download section).

  • If you are compiling OpenEB, some models can be found in <SRC_DIR>/sdk/modules/core_ml/python/models.

  • If you are compiling SDK Pro, some other models can be found in <SRC_DIR>/sdk/modules/ml/python/models and <SRC_DIR>/sdk/modules/ml/python_extended/models.

Event-to-Video Model

  • e2v.ckpt: Pytorch Event to Video pre-trained model which can be used in event to video inference sample The model was trained on a dataset of synthetic events sequences generated by simulation on randomly moving images.

Corner Detection Model

  • corner_detection_10_heatmaps.ckpt: Pytorch Corner Detection pre-trained model which can be used in Demo of Corner Detection Model The model was trained on a dataset of synthetic events sequences generated by simulation on randomly moving images. It takes as input a tensor of 10 channels and outputs 10 heatmaps.

Object Detection Models

  • red_event_cube_05_2020.zip: Object Detection TorchScript model, trained with event_cube preprocessing method. This model can be used with the C++ Detection & Tracking sample and the Python Detection & Tracking sample. This model was trained on a dataset recorded by a camera positioned on top of a car facing forward, thus the performance of the model can be degraded in other settings.

  • red_event_cube_all_classes.ckpt: Pytorch object detection model, which can be exported to torchjit model using export_detector Python sample. Note that this model was not trained as much as the TorchScript models listed above, and is mostly delivered to try-out the our export sample.

Flow Models

Classification Models

Pre-Trained models for rock-paper-scissors classification dataset: