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
File size: 863 Bytes
90f37d6 | 1 2 3 4 5 6 7 8 9 10 11 12 | /**:
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 |