feat: add traffic_light_fine_detector v3.0 artifacts (from awf.ml.dev.web.auto/perception/models/tlr_yolox_s/v3)

.gitignore

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+# Auto-generated TensorRT artifacts, built locally by Autoware from the ONNX
+# files (see autoware_tensorrt_common). They are environment-specific
+# (GPU arch + TensorRT version) and must not be committed to this repo.
+*.engine
+*.json

README.md

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+---
+license: apache-2.0
+pipeline_tag: object-detection
+tags:
+  - autoware
+  - ros2
+  - autonomous-driving
+  - camera
+  - traffic-light
+  - yolox
+  - tensorrt
+  - onnx
+---
+
+# YOLOX-s Traffic Light Fine Detector for Autoware (`traffic_light_fine_detector`)
+
+Camera-based traffic light detection models, used by the
+[`autoware_traffic_light_fine_detector`](https://github.com/autowarefoundation/autoware_universe/tree/main/perception/autoware_traffic_light_fine_detector)
+node in [Autoware](https://github.com/autowarefoundation/autoware).
+
+The models are **YOLOX-s** [1] detectors fine-tuned by TIER IV for traffic light detection. Given the full
+camera image and the coarse ROIs predicted by the `traffic_light_map_based_detector` node, the fine detector
+refines each ROI to a tight bounding box around the traffic light. The models are exported as ONNX; Autoware
+builds the TensorRT engine from the ONNX file on first launch.
+
+## Model overview
+
+| | |
+| --- | --- |
+| Task | 2D traffic light detection (ROI refinement) in camera images |
+| Architecture | YOLOX-s |
+| Detected classes | `traffic_light`, `pedestrian_traffic_light` (plus a `BACKGROUND` label) |
+| Runtime | TensorRT (FP16 by default, FP32 selectable) via the `autoware_traffic_light_fine_detector` ROS 2 node |
+| Format | ONNX (Autoware builds the TensorRT engine locally on first launch) |
+| License | Apache-2.0 |
+
+## Variants in this repository
+
+The three ONNX files share the same weights and differ only in their static batch size. The TensorRT engine
+requires a fixed batch size, so the node pads the ROI list with dummy entries up to the model's batch size
+when fewer traffic lights are visible.
+
+| Variant | Batch size | Default in Autoware |
+| --- | --- | --- |
+| `tlr_car_ped_yolox_s_batch_1.onnx` | 1 | |
+| `tlr_car_ped_yolox_s_batch_4.onnx` | 4 | |
+| `tlr_car_ped_yolox_s_batch_6.onnx` | 6 | yes (loaded by the package's launch file) |
+
+## Files
+
+| File | Description |
+| --- | --- |
+| `tlr_car_ped_yolox_s_batch_1.onnx` | YOLOX-s traffic light detector, static batch size 1 |
+| `tlr_car_ped_yolox_s_batch_4.onnx` | YOLOX-s traffic light detector, static batch size 4 |
+| `tlr_car_ped_yolox_s_batch_6.onnx` | YOLOX-s traffic light detector, static batch size 6 (default) |
+| `tlr_labels.txt` | Class labels: `BACKGROUND`, `traffic_light`, `pedestrian_traffic_light` |
+| `deploy_metadata.yaml` | Deployment metadata recording the artifact version of this repository |
+
+> **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`).
+
+## Inputs and outputs (as used by the node)
+
+**Inputs**
+
+| Topic | Type | Description |
+| --- | --- | --- |
+| `~/input/image` | `sensor_msgs/msg/Image` | The full size camera image |
+| `~/input/rois` | `tier4_perception_msgs/msg/TrafficLightRoiArray` | Coarse ROIs from the map-based detector |
+| `~/expect/rois` | `tier4_perception_msgs/msg/TrafficLightRoiArray` | Offset-free ROIs used to select the best detections |
+
+**Outputs**
+
+| Topic | Type | Description |
+| --- | --- | --- |
+| `~/output/rois` | `tier4_perception_msgs/msg/TrafficLightRoiArray` | The refined, accurate ROIs |
+| `~/debug/exe_time_ms` | `autoware_internal_debug_msgs/msg/Float32Stamped` | Inference time |
+
+If no traffic light is detected inside a coarse ROI, the output ROI's `x_offset`, `y_offset`, `height`, and
+`width` are set to `0`. Detections from YOLOX are matched against the `expect/rois` array as a whole set, not
+each ROI in isolation. Key node parameters (defaults): `precision: fp16`, `score_thresh: 0.3`,
+`nms_thresh: 0.65`, `gpu_id: 0`.
+
+## Usage in Autoware
+
+The node expects these artifacts under `~/autoware_data/ml_models/traffic_light_fine_detector/` and launches
+with, e.g.:
+
+```bash
+ros2 launch autoware_traffic_light_fine_detector traffic_light_fine_detector.launch.xml \
+  data_path:=$HOME/autoware_data/ml_models \
+  model_path:=$HOME/autoware_data/ml_models/traffic_light_fine_detector/tlr_car_ped_yolox_s_batch_6.onnx \
+  label_path:=$HOME/autoware_data/ml_models/traffic_light_fine_detector/tlr_labels.txt
+```
+
+Add `build_only:=true` to build the TensorRT engine from the ONNX as a one-off pre-task.
+See the [package README](https://github.com/autowarefoundation/autoware_universe/tree/main/perception/autoware_traffic_light_fine_detector)
+for the full parameter reference.
+
+## Training
+
+- Base model: YOLOX-s from the official [YOLOX repository](https://github.com/Megvii-BaseDetection/YOLOX),
+  starting from the published [yolox_s.pth](https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_s.pth)
+  pretrained weights.
+- Fine-tuning: performed by TIER IV on around 17,000 internal images of Japanese traffic lights.
+- Further training details (epochs, hyperparameters, evaluation metrics) are not publicly documented.
+
+## Limitations
+
+- Fine-tuned on Japanese traffic lights; accuracy on traffic lights with different shapes, layouts, or
+  signaling conventions may drop without additional fine-tuning.
+- The node is designed to run downstream of the map-based detector: it refines externally provided ROIs
+  rather than detecting traffic lights anywhere in the frame.
+- The TensorRT engine has a static batch size; scenes with more simultaneous ROIs than the model's batch size
+  are processed in multiple inference passes.
+
+## Provenance
+
+| | |
+| --- | --- |
+| Original source | `https://awf.ml.dev.web.auto/perception/models/tlr_yolox_s/v3/` |
+| Version tag in this repository | `v3.0` |
+
+## Citation
+
+```bibtex
+@article{ge2021yolox,
+  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.
+- YOLOX repository: <https://github.com/Megvii-BaseDetection/YOLOX>

deploy_metadata.yaml

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+version: v3.0

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tlr_labels.txt

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+BACKGROUND
+traffic_light
+pedestrian_traffic_light