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@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+assets/PR_curve.png filter=lfs diff=lfs merge=lfs -text
+assets/confusion_matrix_normalized.png filter=lfs diff=lfs merge=lfs -text

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README.md

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+---
+license: cc-by-4.0
+base_model:
+- Ultralytics/YOLOv8
+pipeline_tag: object-detection
+library_name: ultralytics
+github: https://github.com/rfonod/geo-trax
+language:
+- en
+tags:
+- yolov8
+- object-detection
+- aerial-imagery
+- drone
+- vehicle-detection
+- birds-eye-view
+- geo-trax
+- trajectory
+- urban-traffic
+- tracking
+- arxiv:2411.02136
+datasets:
+- rfonod/songdo-vision
+- Voxel51/VisDrone2019-DET
+- detection-datasets/coco
+model-index:
+- name: rfonod/geo-trax/geotrax_hbb_yolov8s_1920_v1
+  results:
+  - task:
+      type: object-detection
+    dataset:
+      type: rfonod/songdo-vision
+      name: Songdo Vision
+      split: test
+    metrics:
+      - type: precision  # mAP@0.5 not available as a standard metric type on HF
+        value: 0.951
+        name: mAP@0.5
+      - type: precision
+        value: 0.711
+        name: mAP@0.5:0.95
+      - type: precision
+        value: 0.911
+        name: Precision
+      - type: recall
+        value: 0.935
+        name: Recall
+---
+
+# Geo-trax: YOLOv8s Vehicle Detector for Drone BEV Imagery
+
+[![GitHub](https://img.shields.io/badge/GitHub-geo--trax-blue?logo=github)](https://github.com/rfonod/geo-trax)
+[![PyPI](https://img.shields.io/pypi/v/geo-trax)](https://pypi.org/project/geo-trax/)
+[![Paper](https://img.shields.io/badge/Journal-10.1016%2Fj.trc.2025.105205-blue)](https://doi.org/10.1016/j.trc.2025.105205)
+[![arXiv](https://img.shields.io/badge/arXiv-2411.02136-b31b1b)](https://arxiv.org/abs/2411.02136)
+[![Zenodo](https://img.shields.io/badge/Zenodo-Software%20Archive-blue)](https://zenodo.org/doi/10.5281/zenodo.12119542)
+[![YouTube](https://img.shields.io/badge/YouTube-Demo-red?logo=youtube&logoColor=red)](https://youtu.be/gOGivL9FFLk)
+[![Website](https://img.shields.io/badge/REAL%20Lab-Geo--trax-informational)](https://www.real-lab.ch/geo-trax)
+[![License](https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey)](LICENSE)
+
+This is the default detection model for **[Geo-trax](https://github.com/rfonod/geo-trax)**, a
+comprehensive pipeline for extracting georeferenced vehicle trajectories from high-altitude drone
+(bird's-eye view) video footage. The model detects vehicles in aerial imagery and underpins the
+results reported in the associated [publication](https://doi.org/10.1016/j.trc.2025.105205).
+
+![Geo-trax Output Visualization](https://raw.githubusercontent.com/rfonod/geo-trax/v1.0.0/assets/geo-trax_visualization.webp)
+
+🎬 This accelerated animation previews some of the capabilities of Geo-trax. Watch the full
+demonstration (~4 min) on [YouTube](https://youtu.be/gOGivL9FFLk).
+
+## Model Details
+
+| Property | Value |
+|---|---|
+| Architecture | YOLOv8s (HBB, horizontal bounding boxes) |
+| Input resolution | 1920 × 1920 px |
+| Classes | 6 trained (4 primary + 2 auxiliary; see below) |
+| Parameters | ~11 M |
+| Framework | [Ultralytics](https://github.com/ultralytics/ultralytics) ≥ 8.4.64 |
+| Trained on | 19,339 annotated aerial images (679,306 labeled instances); multi-stage, see [publication](https://doi.org/10.1016/j.trc.2025.105205) |
+| Validated on | [Songdo Vision](https://doi.org/10.5281/zenodo.13828407) test set (1,084 images, 55,124 vehicle instances) |
+
+### Classes and Detection Performance
+
+Metrics reported on the [Songdo Vision](https://doi.org/10.5281/zenodo.13828407) test split
+(1,084 images, 55,124 labeled vehicle instances). The `Instances` column is the per-class
+support in the test set. See Table 3 of the
+[publication](https://doi.org/10.1016/j.trc.2025.105205) for full results.
+
+| ID | Label | Notes | Instances | Precision | Recall | mAP@50 | mAP@50-95 |
+|---|---|---|---:|---|---|---|---|
+| 0 | Car | incl. vans | 49,508 | 0.979 | 0.981 | 0.992 | 0.835 |
+| 1 | Bus | | 1,759 | 0.952 | 0.977 | 0.988 | 0.826 |
+| 2 | Truck | | 3,052 | 0.887 | 0.916 | 0.935 | 0.722 |
+| 3 | Motorcycle | | 805 | 0.827 | 0.866 | 0.888 | 0.463 |
+| 4 | Pedestrian | not evaluated | n/a | n/a | n/a | n/a | n/a |
+| 5 | Bicycle | not evaluated | n/a | n/a | n/a | n/a | n/a |
+| **All** | | | **55,124** | **0.911** | **0.935** | **0.951** | **0.711** |
+
+The model reaches **0.951 mAP@50** and **0.711 mAP@50-95** overall, with near-saturated accuracy
+on cars and buses (mAP@50 ≥ 0.988). Trucks and especially motorcycles are harder: motorcycles are
+small, sparse in the test set (805 instances), and the main driver of the lower mAP@50-95.
+
+### Evaluation Plots
+
+Precision-recall curves and the normalized confusion matrix on the Songdo Vision test set:
+
+<table>
+  <tr>
+    <td align="center"><img src="assets/PR_curve.png" alt="Precision-Recall Curve" width="420"><br><sub>Precision-Recall Curve</sub></td>
+    <td align="center"><img src="assets/confusion_matrix_normalized.png" alt="Normalized Confusion Matrix" width="420"><br><sub>Normalized Confusion Matrix</sub></td>
+  </tr>
+</table>
+
+> **Note on pedestrian and bicycle classes:** The model was trained on pedestrian and bicycle
+> instances; however, these classes are **not evaluated and not recommended for use**. They were
+> underrepresented in the training data, are not annotated in the Songdo Vision dataset (making
+> reliable evaluation impossible), and achieve poor detection performance in practice.
+
+## How to Use
+
+### With Geo-trax (recommended)
+
+This model is the default in Geo-trax and downloads automatically on first use:
+
+```bash
+pip install geo-trax
+geotrax extract video.mp4           # detect, track, and stabilize; auto-downloads the model
+geotrax batch   video.mp4 --no-geo  # detect, track, and stabilize; skip georeferencing
+geotrax batch   video.mp4           # full pipeline including georeferencing (requires orthophotos)
+```
+
+See the Geo-trax [GitHub README](https://github.com/rfonod/geo-trax) for the full pipeline,
+configuration options, and georeferencing.
+
+### Direct Ultralytics inference
+
+```python
+from ultralytics import YOLO
+from huggingface_hub import hf_hub_download
+
+weights = hf_hub_download(repo_id="rfonod/geo-trax", filename="geotrax_hbb_yolov8s_1920_v1.pt")
+model = YOLO(weights)
+
+results = model("drone_frame.jpg", imgsz=1920, conf=0.25, iou=0.45, classes=[0, 1, 2, 3])
+results[0].show()
+```
+
+> **Tip:** The model was trained and validated at **1920 px** input resolution. Downscaling to
+> 1280 px is possible with a small accuracy trade-off; going below 960 px significantly degrades
+> detection of small vehicles (motorcycles, distant cars). Pass `classes=[0, 1, 2, 3]` to
+> restrict inference to the four evaluated classes and suppress unreliable predictions.
+
+## Training Data
+
+Training followed a multi-stage strategy starting from **YOLOv8s weights pretrained on COCO**
+as the initial foundation. Two successive stages were applied:
+
+**Stage 1 (BASE):** The model was trained on a large, diverse collection drawn from eight
+public aerial and drone datasets (CARPK, PUCPR+, CyCAR, UAVDT, HARPY, RAI4VD, UIT-ADrone,
+and VisDrone) combined with the [Songdo Vision](https://doi.org/10.5281/zenodo.13828407)
+dataset, totalling 19,339 training images with 679,306 annotations across 6 vehicle classes
+(car, bus, truck, motorcycle, pedestrian, bicycle).
+
+**Stage 2 (FINE):** The BASE-trained model was subsequently fine-tuned on a curated,
+high-quality subset of 9,004 images with 321,368 annotations, emphasising accurate annotations
+and higher-resolution images, again combined with
+[Songdo Vision](https://doi.org/10.5281/zenodo.13828407), to yield the final weights released here.
+
+**Training set composition** (annotations per class):
+
+| Stage | Images | Annotations | Car | Bus | Truck | Motorcycle | Pedestrian | Bicycle |
+|---|---:|---:|---:|---:|---:|---:|---:|---:|
+| BASE | 19,339 | 679,306 | 561,666 | 15,587 | 28,830 | 44,512 | 24,239 | 4,472 |
+| FINE |  9,004 | 321,368 | 266,745 |  8,047 | 14,305 | 30,925 |  1,260 |    86 |
+
+[Songdo Vision](https://doi.org/10.5281/zenodo.13828407) comprises 5,419 annotated drone frames
+(4,335 training / 1,084 test; 80/20 split) collected during a large-scale urban traffic
+monitoring experiment in Songdo, South Korea. It covers four primary vehicle classes captured
+at 140-150 m altitude by DJI Mavic 3 drones, contributing 217,311 training and 55,124 test
+instances to the totals above.
+
+**Training configuration:**
+
+| Setting | Value |
+|---|---|
+| Initialization | YOLOv8s pretrained on COCO |
+| Optimizer | SGD |
+| Learning rate (initial / final factor) | 0.01 / 0.01 |
+| Momentum | 0.937 |
+| Weight decay | 0.0005 |
+| Batch size | 8 |
+| Early stopping | 50-epoch patience |
+| Input resolution | 1920 × 1920 px (letterbox padding) |
+| Mixed precision | AMP enabled |
+| Augmentation | random scaling, translation, horizontal flip, mosaic, colour jitter, Gaussian/median blur, grayscale, CLAHE |
+
+See the [publication](https://doi.org/10.1016/j.trc.2025.105205) for complete dataset statistics,
+training details, and ablation results.
+
+## Intended Use and Limitations
+
+- **GSD assumption:** The bundled Geo-trax config assumes a ground sampling distance (GSD) of
+  ~0.027 m/px (DJI Mavic 3, 4K, 140-150 m altitude). Adjust this value in the config for
+  different hardware or flight altitudes.
+- **Supported classes:** Car, bus, truck, and motorcycle (class IDs 0-3). The model was also
+  trained on pedestrian and bicycle instances; however, these classes achieve poor detection
+  performance and are not recommended for use (see the class table above). Geo-trax filters to
+  the four primary classes by default; when using Ultralytics directly, pass
+  `classes=[0, 1, 2, 3]` to suppress unreliable predictions.
+
+## Citation
+
+If you use this model, please cite the associated publication:
+
+```bibtex
+@article{fonod2025advanced,
+  title   = {Advanced computer vision for extracting georeferenced vehicle trajectories from drone imagery},
+  author  = {Fonod, Robert and Cho, Haechan and Yeo, Hwasoo and Geroliminis, Nikolas},
+  journal = {Transportation Research Part C: Emerging Technologies},
+  volume  = {178},
+  pages   = {105205},
+  year    = {2025},
+  doi     = {10.1016/j.trc.2025.105205}
+}
+```
+
+If you additionally use the [Geo-trax software](https://github.com/rfonod/geo-trax), please
+also cite the specific version you used via its Zenodo record. For example, for version 1.0.0:
+
+```bibtex
+@software{fonod2026geo-trax,
+  author  = {Fonod, Robert},
+  title   = {Geo-trax: A Comprehensive Framework for Georeferenced Vehicle Trajectory Extraction from Drone Imagery},
+  url     = {https://github.com/rfonod/geo-trax},
+  doi     = {10.5281/zenodo.12119542},
+  version = {1.0.0},
+  year    = {2026}
+}
+```
+
+## License
+
+This model is released under the
+[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)
+license; see the [LICENSE](LICENSE) file for the full terms. The Geo-trax codebase is distributed
+separately under the [MIT License](https://github.com/rfonod/geo-trax/blob/main/LICENSE).

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