YOLOv8s Finetuned on KITTI

Fine-tuned YOLOv8s object detector on the KITTI benchmark dataset, trained and evaluated as part of DetectionBench -- a framework for reproducibly benchmarking modern object detectors with identical training recipes and evaluation metrics across multiple real-world datasets.

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Usage

Install Dependencies

pip install ultralytics huggingface_hub

Load Model from Hugging Face

from huggingface_hub import hf_hub_download
from ultralytics import YOLO

weights = hf_hub_download(
    repo_id="dronefreak/kitti-yolov8s",
    filename="best.pt"
)

model = YOLO(weights)

Run Inference

results = model.predict(
    source="image.jpg",
    conf=0.25
)

results[0].show()

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Performance

Evaluated on the KITTI val split, using DetectionBench's standard evaluation pipeline (detectionbench-evaluate).

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KITTI Model Zoo

Every model DetectionBench has trained and evaluated on KITTI so far, for full transparency -- see DetectionBench for the smaller, curated comparison set used on the project README.

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Per-Class Performance

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Dataset

This model was trained on KITTI. For the full dataset description, provenance, license, and citation, see the dataset card:

https://huggingface.co/datasets/dronefreak/KITTI

Classes

• Car
• Cyclist
• Misc
• Pedestrian
• Person_sitting
• Tram
• Truck
• Van

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Training Configuration

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Repository Contents

best.pt
results.csv
args.yaml
BoxPR_curve.png
BoxF1_curve.png
BoxP_curve.png
BoxR_curve.png
confusion_matrix.png
confusion_matrix_normalized.png
val_batch0_pred.jpg
kitti_yolov8s_showcase.jpg
assets/demo_banner.mp4
assets/demo_banner_poster.jpg
README.md

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Related Resources

• KITTI dataset card on Hugging Face
• DetectionBench -- reproducible benchmarks for modern object detectors on real-world datasets
• KITTI paper (CVPR 2012, doi:10.1109/CVPR.2012.6248074)
• KITTI Vision Benchmark Suite (official data source)

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Training Framework

This model was trained using DetectionBench, an open-source framework for benchmarking object detectors across multiple real-world datasets with a common pipeline.

Features include:

• A dataset-adapter registry for converting real-world datasets into a canonical format
• Identical training/evaluation recipes across model families (Ultralytics YOLO/RT-DETR, RF-DETR)
• Hardware profiling (latency, FPS, VRAM, parameters, FLOPs)
• One-command reproducibility via versioned Hydra configs

If you find this model useful, please consider starring the repository.

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Known Limitations

• No official test-set labels: KITTI's real held-out test images have never had public ground truth, so this adapter (following the field-standard Chen et al. 2015 3DOP split) uses train (3,712) / valid (3,769) only -- "valid" is both the early-stopping signal and the split all metrics on this card are computed on, the same convention the wider KITTI detection literature uses.
• Severe class imbalance across 8 classes: Car is 70.8% of all boxes, while Person_sitting has only 222 instances (0.5%) across all 7,481 images -- its per-class score is measured on very few examples and should be read with caution.
• Unusual native aspect ratio: frames are ~1242x375 (~3.3:1, not the usual 4:3/16:9), so square-letterboxed training/inference wastes canvas on padding above and below the real content; a higher input resolution (see Training Configuration) partly compensates for the resulting loss of effective resolution.
• Small dataset for the task's difficulty: only 3,712 training images across a real (non-memorization-prone) street-scene detection task, so absolute scores are lower than on datasets with more training data or an easier task shape.
• Demo banner is not KITTI's own labelled data: the video above uses KITTI's official but unlabelled tracking-benchmark sequences (0000-0028.mp4) for illustration, run through the model at inference time -- it is not part of the train/valid split and the boxes shown are the model's raw predictions, not checked against ground truth.

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Citation

If you use this model in your research, please consider citing the dataset and the model architecture:

@inproceedings{geiger2012kitti,
  title={Are we ready for autonomous driving? The KITTI vision benchmark suite},
  author={Geiger, Andreas and Lenz, Philip and Urtasun, Raquel},
  booktitle={2012 IEEE Conference on Computer Vision and Pattern Recognition},
  pages={3354--3361},
  year={2012},
  organization={IEEE},
  doi={10.1109/CVPR.2012.6248074}
}

No official YOLOv8 research paper has been published by Ultralytics; this is their own recommended software citation instead:

@software{jocher2023yolov8,
  author = {Glenn Jocher and Ayush Chaurasia and Jing Qiu},
  title = {Ultralytics YOLOv8},
  version = {8.0.0},
  year = {2023},
  url = {https://github.com/ultralytics/ultralytics},
  license = {AGPL-3.0}
}