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Qcar2 Ml Steering Weights

Weights and metadata for the QCar2 ROS 2 ML steering stack used in the rosbotws lane-keeping and LiDAR obstacle-avoidance project.

Sourcehuggingface.co/HammadNaseer/qcar2-ml-steering-weights/tree/9c293a98e651fc05ef3fcf2c7e49646cc58d04ca
KindOtherVersionRevision 9c293a9Bgrade

Model source

Kind
Other
File size
6.47–124 MB
Version
vRevision 9c293a9
Updated
Oct 10, 2026
Downloads
9
Source
Hugging Face

Source description

QCar2 ML Steering Weights

Weights and metadata for the QCar2 ROS 2 ML steering stack used in the rosbot_ws lane-keeping and LiDAR obstacle-avoidance project.

The primary model is an RF-DETR segmentation ONNX export used by rfdetr_onnx_lane_node to detect the lane mask and publish a steering target. The repository also includes fallback local weights used by the alternate YOLO/DL and ResNet lane-keeping modes in the workspace.

Files

FilePurpose
car_track_v3_lane.onnxPrimary RF-DETR ONNX segmentation model.
car_track_v3_lane.classes.txtClass names in model output order.
class_names.jsonStructured class-id mapping.
model_config.jsonInput/output, runtime, and ROS usage metadata.
eval/onnx_runtime_smoke_test.jsonONNX Runtime smoke-test output and top scores.
eval/rfdetr_sample_predictions.jsonSample RF-DETR prediction from the project.
fallback/best.ptFallback YOLO/DL segmentation weight from the ROS package.
fallback/resnet18_road_following.pthFallback ResNet18 road-following weight.
checksums.sha256SHA256 checksums for uploaded binary weights.
NOTES.mdOriginal local packaging and verification notes.

Class Names

Class IDName
0background_class83422
1lane2
2traffic_light

Primary Model Details

  • Architecture: RF-DETR segmentation medium
  • Task: lane and traffic-light segmentation for QCar2 track perception
  • Runtime: ONNX Runtime CPU or CUDA provider
  • Input tensor: input, float32 NCHW [1, 3, 432, 432]
  • Outputs:
    • dets: [1, 200, 4], normalized cx, cy, w, h
    • labels: [1, 200, 3], class logits
    • 4647: [1, 200, 108, 108], mask logits

Sample Scores

Sample project inference from eval/rfdetr_sample_predictions.json:

ClassConfidenceBBox XYXY
lane20.9720[6, 99, 640, 480]

ONNX Runtime smoke-test top prediction from eval/onnx_runtime_smoke_test.json:

ClassScoreNormalized Box
lane20.8555[0.5314, 0.5985, 0.9405, 0.7918]

These are smoke-test/sample scores, not a full benchmark.

Intended Use

This repository is intended for the QCar2 ROS 2 simulation/autonomy stack:

  • RF-DETR ONNX lane target publishing
  • ML lane keeping
  • LiDAR-triggered obstacle avoidance
  • one-way lane switching around obstacles

It is not a production autonomous-driving model and should not be used as a real vehicle safety system.

Download

hf download HammadNaseer/qcar2-ml-steering-weights \
  car_track_v3_lane.onnx car_track_v3_lane.classes.txt \
  --local-dir weights

ROS 2 Usage

Expected workspace paths:

weights/car_track_v3_lane.onnx
weights/car_track_v3_lane.classes.txt

The main script uses those files with:

scripts/run_autonomy.sh

or through:

LANE_KEEPER=rfdetr scripts/run_lane_keeping.sh

Checksums

c981e0652eb1c268f1fa28f7cc4d51e8df8b9064d78c7a3e3b84dfda0d762fbd  car_track_v3_lane.onnx
946e7817a5c809486fb5d467dfe1aca3746bf8684a9367894b3dbec9ebcdb7b8  car_track_v3_lane.classes.txt
5814d98ba5d313e624467d06bffc0b305e8586870fc431317f7a3f35451c060d  fallback/best.pt
774f4e7405ca050a7a8536e8eafa077fb18bbfc009364c3937d97d68866e0454  fallback/resnet18_road_following.pth

Limitations

  • Trained/exported for this QCar2 simulation project and camera viewpoint.
  • Sample scores are from local smoke tests, not a held-out validation set.
  • Performance outside the lab track simulation is not guaranteed.
  • License is marked other until the exact upstream dataset/export license is confirmed.

Sources

1 source
Hugging FacePrimaryVersion 9c293a98e651fc05ef3fcf2c7e49646cc58d04ca
huggingface.co/HammadNaseer/qcar2-ml-steering-weights/tree/9c293a98e651fc05ef3fcf2c7e49646cc58d04ca

Verified Oct 10

Download files

Model artifacts

3 artifacts

car_track_v3_lane.onnx

onnx · 124 MB · SHA-256 c981e0652eb1…2fbd · Hugging Face

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fallback/best.pt

pt · 6.47 MB · SHA-256 5814d98ba5d3…060d · Hugging Face

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fallback/resnet18_road_following.pth

pth · 42.7 MB · SHA-256 774f4e7405ca…0454 · Hugging Face

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Source excerpts

3 excerpts
ReadmeAug 1huggingface.co/HammadNaseer/qcar2-ml-steering-weights

--- library_name: onnx pipeline_tag: image-segmentation tags: - onnx - onnxruntime - robotics - ros2 - qcar2 - autonomous-driving - lane-detection - obstacle-avoidance - rfdetr license: other --- # QCar2 ML Steering Weights Weights and metadata for the QCar2 ROS 2 ML steering stack used in the `rosbot_ws` lane-keeping and LiDAR obstacle-avoidance project. The primary model is an RF-DETR segmentation ONNX export used by `rfdetr_onnx_lane_node` to detect the lane mask and publish a steering target. The repository also includes fallback local weights used by the alternate YOLO/DL and ResNet lane-keeping modes in the workspace. ## Files | File | Purpose | |---|---| | `car_track_v3_lane.onnx` | Primary RF-DETR ONNX segmentation model. | | `car_track_v3_lane.classes.txt` | Class names in model output order. | | `class_names.json` | Structured class-id mapping. | | `model_config.json` | Input/output, runtime, and ROS usage metadata. | | `eval/onnx_runtime_smoke_test.json` | ONNX Runtime smoke-test output and top scores. | | `eval/rfdetr_sample_predictions.json` | Sample RF-DETR prediction from the project. | | `fallback/best.pt` | Fallback YOLO/DL segmentation weight from the ROS package. | | `fallback/resnet18_road_following.pth` | Fallback ResNet18 road-following weight. | | `checksums.sha256` | SHA256 checksums for uploaded binary weights. | | `NOTES.md` | Original local packaging and verification notes. | ## Class Names | Class ID | Name | |---:|---| | 0 | `background_class83422` | | 1 | `lane2` | | 2 | `traffic_light` | ## Primary Model Details - Architecture: RF-DETR segmentation medium - Task: lane and traffic-light segmentation for QCar2 track perception - Runtime: ONNX Runtime CPU or CUDA provider - Input tensor: `input`, float32 NCHW `[1, 3, 432, 432]` - Outputs: - `dets`: `[1, 200, 4]`, normalized `cx, cy, w, h` - `labels`: `[1, 200, 3]`, class logits - `4647`: `[1, 200, 108, 108]`, mask logits ## Sample Scores Sample project inference from `eval/rfdetr_sample_predictions.json`: | Class | Confidence | BBox XYXY | |---|---:|---| | `lane2` | 0.9720 | `[6, 99, 640, 480]` | ONNX Runtime smoke-test top prediction from `eval/onnx_runtime_smoke_test.json`: | Class | Score | Normalized Box | |---|---:|---| | `lane2` | 0.8555 | `[0.5314, 0.5985, 0.9405, 0.7918]` | These are smoke-test/sample scores, not a full benchmark. ## Intended...

NoteOct 10huggingface.co/HammadNaseer/qcar2-ml-steering-weights

Source context: 9 downloads · 0 likes · Pipeline image-segmentation · Library onnx · Repo HammadNaseer/qcar2-ml-steering-weights

NoteAug 1huggingface.co/HammadNaseer/qcar2-ml-steering-weights

Source context: 23 downloads · 0 likes · Pipeline image-segmentation · Library onnx · Repo HammadNaseer/qcar2-ml-steering-weights