AutowareFoundation/tensorrt_yolox

YOLOX for Autoware (tensorrt_yolox)

2D object detection (and optional semantic segmentation) models for camera images, used by the `autoware_tensorrt_yolox` node in Autoware.

The models follow the YOLOX [1] architecture. They are exported as ONNX so they can be deployed across hardware; Autoware builds the TensorRT engine from the ONNX file on first launch. The same node binary consumes every model in this repository: the camera object detection pipeline uses the detection and detection+segmentation models, and a separate node instance runs the whole-image traffic light detector that feeds the traffic light recognition pipeline.

Model overview

Labels listed in the model's label file that are not in the node's known set are reported as UNKNOWN. yolox-tiny.onnx has an EfficientNMS_TRT module attached after the network to accelerate non-maximum suppression; the module contains fixed score_threshold and nms_threshold values, so those node parameters are ignored for models that include it.

Model families in this repository

Notes on the families:

• —yolox-sPlus-opt-pseudoV2-T4-960x960-T4-seg16cls is a multi-header model based on YOLOX-s, tuned to detect more accurately than yolox-tiny at almost comparable execution speed. Besides detection it outputs a semantic segmentation mask used for point cloud filtering. Recommended settings: precision:=int8, calibration_algorithm:=Entropy, clip_value:=6.0 (these are the defaults in yolox_s_plus_opt.param.yaml).
• —yolox-sPlus-T4-960x960-pseudo-finetune is the detection-only alternative offered by the same launch file.
• —The traffic light detector localizes car and pedestrian traffic lights on the full camera image; its labels come from car_ped_tl_detector_labels.txt (BACKGROUND, traffic_light, pedestrian_traffic_light).
• —label.txt for the detection models contains UNKNOWN, CAR, TRUCK, BUS, BICYCLE, MOTORBIKE, PEDESTRIAN, ANIMAL.

The semantic segmentation mask is a gray image where each pixel holds a class index; semseg_color_map.csv maps the 16 indices to names and RGB colors for visualization (others, building (spelled buildling in the shipped CSV), wall, obstacle, trafficlight, trafficsign, person, vehicle, bike, road, sidewalk, roadPaint, curbstone, crosswalk_others, vegetation, sky).

Files

TensorRT engines are not distributed here. TensorRT engines are specific to the GPU architecture and TensorRT version they are built on and are not portable, so Autoware builds them locally from the ONNX files on first launch (or via build_only:=true). Engines are saved next to the ONNX files with an .engine extension and reused on subsequent runs; the first build typically takes 10 to 20 minutes. The EntropyV2-calibration.table files are used by the node when running with precision:=int8.

Inputs and outputs (as used by the node)

Input: ~/in/image (sensor_msgs/msg/Image), the camera image.

Outputs:

• —~/out/objects (tier4_perception_msgs/msg/DetectedObjectsWithFeature): detected objects or traffic lights with 2D bounding boxes.
• —~/out/image (sensor_msgs/msg/Image): input image with 2D bounding boxes drawn, for visualization.
• —~/out/mask (sensor_msgs/msg/Image): semantic segmentation mask (semantic segmentation model only).
• —~/out/color_mask (sensor_msgs/msg/Image): colorized segmentation mask for visualization (semantic segmentation model only).

Usage in Autoware

The node reads these artifacts from $HOME/autoware_data/ml_models/tensorrt_yolox/ by default and launches with, e.g.:

# Detection + semantic segmentation (default), or detection-only via model_path
ros2 launch autoware_tensorrt_yolox yolox_s_plus_opt.launch.xml

# Lightweight detection
ros2 launch autoware_tensorrt_yolox yolox_tiny.launch.xml

# Whole-image traffic light detection
ros2 launch autoware_tensorrt_yolox yolox_traffic_light_detector.launch.xml

Add build_only:=true to build the TensorRT engine from the ONNX as a one-off pre-task. See the package README for the full parameter reference, including precision selection (fp32, fp16, int8) and INT8 calibration options.

Training

The models are based on the official YOLOX implementation. The training datasets, schedules, and evaluation metrics of the T4 finetuned variants are not publicly documented.

• —YOLOX (architecture and training framework): <https://github.com/Megvii-BaseDetection/YOLOX>
• —trt-yoloXP (TIER IV YOLOX extensions): <https://github.com/tier4/trt-yoloXP>
• —yoloxonnxmodifier (embeds EfficientNMS_TRT into exported ONNX): <https://github.com/wep21/yoloxonnxmodifier>

The consuming package documents how to export custom YOLOX models to ONNX (plain or with EfficientNMS_TRT) in its README.

Provenance

Original hosting before migration to Hugging Face (tag v1.0 bundles all four families):

Limitations

• —Detection models output only the classes listed above; other road users are reported as UNKNOWN.
• —label.txt is incompatible with models that output COCO labels (e.g. models from the official YOLOX repository); those need their own label file.
• —Fixed score_threshold and nms_threshold are baked into models with EfficientNMS_TRT (e.g. yolox-tiny.onnx); the node parameters of the same names have no effect for them.
• —Training data details of the T4 finetuned variants are not publicly documented, so accuracy on sensor setups and environments different from those used for training is not characterized here.

Citation

@article{yolox2021,
  title   = {YOLOX: Exceeding YOLO Series in 2021},
  author  = {Ge, Zheng and Liu, Songtao and Wang, Feng and Li, Zeming and Sun, Jian},
  journal = {arXiv preprint arXiv:2107.08430},
  year    = {2021}
}

References

• —[1] Ge et al., "YOLOX: Exceeding YOLO Series in 2021", arXiv:2107.08430, 2021.
• —Megvii-BaseDetection/YOLOX: <https://github.com/Megvii-BaseDetection/YOLOX>
• —tier4/trt-yoloXP: <https://github.com/tier4/trt-yoloXP>
• —wep21/yoloxonnxmodifier: <https://github.com/wep21/yoloxonnxmodifier>