Object Detection
TensorRT
ONNX
autoware
ros2
autonomous-driving
camera
multi-view
3d-object-detection
streampetr
Instructions to use AutowareFoundation/camera_streampetr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- TensorRT
How to use AutowareFoundation/camera_streampetr with TensorRT:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
| /**: | |
| ros__parameters: | |
| model_params: # These are parameters that are dependent on the onnx file. | |
| class_names: ["CAR", "TRUCK", "BUS", "BICYCLE", "PEDESTRIAN"] | |
| detection_range: [-61.2, -61.2, -10.0, 61.2, 61.2, 10.0] # [-x,-y,-z,x,y,z] from base_link. The model is not trained to predict objects outside this range. | |
| # So, anything outside this range is wrong, and this parameter is used to filter out objects that are outside this range | |
| input_image_height: 480 | |
| input_image_width: 640 | |
| num_proposals: 5400 | |
| rois_number: 5 # ["CAM_FRONT", "CAM_FRONT_LEFT", "CAM_BACK_LEFT", "CAM_FRONT_RIGHT", "CAM_BACK_RIGHT"] | |
| pre_memory_length: 1024 # Input memory length for the temporal model | |
| post_memory_length: 1280 # Output memory length for the temporal model |