Model Card for Model ID
This is a YOLO11 detector fine-tuned on the KITTI Object Detection Benchmark
(training split, 7,481 frames) for an ADAS perception pipeline. See the
full project:
https://github.com/ishaannk/ADAS-Object-Detection-and-Collision-AvoidanceClasses
Car, Van, Truck, Pedestrian, Person_sitting, Cyclist, Tram, Misc — KITTI's
own taxonomy, not remapped to COCO classes.
Training data
KITTI Object Detection Benchmark, training split only. Deterministic 85/15
train/val split (seed 42) over sorted frame ids — not the literature Chen et
al. 3712/3769 split.
Metrics
See metrics.json in this repo for per-class mAP50 / mAP50-95 and
KITTI-protocol-style easy/moderate/hard AP.
Intended use
Research and portfolio demonstration of a calibrated camera-LIDAR fusion +
collision-risk pipeline. Not validated for deployment in a vehicle.
License
Base model (Ultralytics YOLO11) is AGPL-3.0. KITTI's terms restrict this
dataset to non-commercial research use — these weights are not licensed for
commercial/production use as-is.
Model Details
Model Description
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- License: agpl-3.0
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Uses
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Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
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Training Details
Training Data
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Training Procedure
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Evaluation
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Summary
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Environmental Impact
Carbon emissions can be estimated using the
Machine Learning Impact calculator presented in
Lacoste et al. (2019).
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