Quantifying the Carbon Emissions of Machine Learning
Paper • 1910.09700 • Published • 60
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-Avoidance
Classes
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
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Uses
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Bias, Risks, and Limitations
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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
Use the code below 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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Factors
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Metrics
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Results
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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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