dronefreak/kitti-yolo11x

YOLO11x Finetuned on KITTI

Fine-tuned YOLO11x 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.

<!-- Demo banner: side-by-side video of this checkpoint's detections on two KITTI test clips. Media lives under assets/ in this repo. The <video> renders on the Hugging Face model page (absolute resolve/ URL); on GitHub the nested <img> poster is shown instead. --> <p align="center"><video controls autoplay loop muted playsinline width="900" poster="https://huggingface.co/dronefreak/kitti-yolo11x/resolve/main/assets/demobannerposter.jpg" src="https://huggingface.co/dronefreak/kitti-yolo11x/resolve/main/assets/demobanner.mp4"><img src="https://huggingface.co/dronefreak/kitti-yolo11x/resolve/main/assets/demobanner_poster.jpg" alt="YOLO11x detections on two KITTI test clips" width="900"></video></p>

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<!-- ROW 1: Identity & Tech Stack --> <div style="display: flex; justify-content: center; align-items: center; gap: 8px; margin-bottom: 8px; flex-wrap: wrap;"> <img src="https://img.shields.io/badge/Task-ObjectDetection-blue?style=flat-square" alt="Task"> <img src="https://img.shields.io/badge/Framework-UltralyticsYOLO-0aa1a7?style=flat-square" alt="Framework"> <img src="https://img.shields.io/badge/Base_Model-YOLO11x-purple?style=flat-square" alt="Base Model"> </div>

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Performance

Evaluation Protocol

Metrics reported in this model card are computed on the KITTI val split, using DetectionBench's standard evaluation pipeline (detectionbench-evaluate).

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.

Per-Class Performance

Evaluation Visualizations

Precision-Recall Curve

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F1 Curve

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Confusion Matrix

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Normalized Confusion Matrix

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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 ---

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-yolo11x",
    filename="best.pt"
)

model = YOLO(weights)

Run Inference

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

results[0].show()

Training Configuration

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_yolo11x_showcase.jpg
assets/demo_banner.mp4
assets/demo_banner_poster.jpg
README.md

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)

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.

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. ---

Citation

If you use this model in your research, please consider citing:

1. 1.The KITTI dataset (see below)
2. 2.The original YOLO11x architecture (see below)
3. 3.The other model architectures shown in the Model Zoo/External Comparison tables above, if you reference their results
4. 4.DetectionBench, the training/evaluation framework used to produce this checkpoint

@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 YOLO11 research paper has been published by Ultralytics; the most commonly cited independent architectural analysis is used instead:

@article{khanam2024yolov11,
  title={YOLOv11: An Overview of the Key Architectural Enhancements},
  author={Khanam, Rahima and Hussain, Muhammad},
  journal={arXiv preprint arXiv:2410.17725},
  year={2024}
}

Other architectures compared against on KITTI in this model card:

YOLO26

@article{jocher2026yolo26,
  title={Ultralytics YOLO26: Unified Real-Time End-to-End Vision Models},
  author={Jocher, Glenn and Qiu, Jing and Liu, Mengyu and Lyu, Shuai and Akyon, Fatih Cagatay and Kalfaoglu, Muhammet Esat},
  journal={arXiv preprint arXiv:2606.03748},
  year={2026}
}

YOLOv8

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}
}

YOLOv9

@article{wang2024yolov9,
  title={YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information},
  author={Wang, Chien-Yao and Yeh, I-Hau and Liao, Hong-Yuan Mark},
  journal={arXiv preprint arXiv:2402.13616},
  year={2024}
}

@software{Saksena_DetectionBench_2026,
  author = {Saksena, Saumya Kumaar},
  title = {DetectionBench: Reproducible Benchmarks for Modern Object Detectors on Real-World Datasets},
  url = {https://github.com/dronefreak/DetectionBench},
  year = {2026}
}