Upload model artifacts

.gitattributes

@@ -33,3 +33,9 @@ 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
+BoxF1_curve.png filter=lfs diff=lfs merge=lfs -text
+BoxP_curve.png filter=lfs diff=lfs merge=lfs -text
+BoxPR_curve.png filter=lfs diff=lfs merge=lfs -text
+BoxR_curve.png filter=lfs diff=lfs merge=lfs -text
+confusion_matrix.png filter=lfs diff=lfs merge=lfs -text
+visdrone_showcase.gif filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,299 @@
+---
+license: apache-2.0
+
+pipeline_tag: object-detection
+
+library_name: ultralytics
+
+datasets:
+  - Voxel51/VisDrone2019-DET
+
+tags:
+  - object-detection
+  - aerial-imagery
+  - drone
+  - visdrone
+  - ultralytics
+  - pytorch
+  - computer-vision
+
+metrics:
+  - map50
+  - map50-95
+  - precision
+  - recall
+  - f1
+
+base_model: "Ultralytics/YOLO11"
+---
+
+
+# YOLOv11l Finetuned on VisDrone
+
+Fine-tuned YOLOv11l object detector for aerial imagery using the VisDrone benchmark dataset.
+
+This model is part of the **VisDrone Detection Model Zoo**, a collection of YOLO models trained and evaluated under a common pipeline for aerial object detection.
+
+## Detection Showcase
+
+<p align="center">
+  <img src="visdrone_showcase.gif" alt="VisDrone Detection Demo">
+</p>
+
+---
+
+## Performance
+
+| Metric     | Score (%)       |
+| ---------- | --------------- |
+| mAP@50     | 37.14     |
+| mAP@50-95  | 21.85   |
+| Precision  | 51.87 |
+| Recall     | 40.33    |
+| F1 Score   | 45.38        |
+| Parameters | -    |
+| FLOPs      | -     |
+
+---
+
+## Evaluation Protocol
+
+Metrics reported in this model card are computed on the VisDrone test set with ground-truth annotations available for evaluation.
+
+---
+
+## VisDrone Model Zoo
+
+| Rank                       | Model                | mAP@50        | mAP@50-95       | Precision         | Recall         |
+| -------------------------- | -------------------- | ------------- | --------------- | ----------------- | -------------- |
+|  |                      |               |                 |                   |                |
+| 1           | YOLOv9e | 40.02 | 23.73 | 54.78 | 42.42 |
+|  |                      |               |                 |                   |                |
+| 2           | YOLOv11x | 38.44 | 22.6 | 52.41 | 41.43 |
+|  |                      |               |                 |                   |                |
+| 3           | YOLOv26x | 38.33 | 22.48 | 52.91 | 41.06 |
+|  |                      |               |                 |                   |                |
+| 4           | YOLOv11l | 37.14 | 21.85 | 51.87 | 40.33 |
+|  |                      |               |                 |                   |                |
+| 5           | YOLOv10x | 37.24 | 21.81 | 52.59 | 39.84 |
+|  |                      |               |                 |                   |                |
+| 6           | YOLOv26l | 37.65 | 21.75 | 51.6 | 40.42 |
+|  |                      |               |                 |                   |                |
+| 7           | YOLOv9c | 37.22 | 21.73 | 51.99 | 39.77 |
+|  |                      |               |                 |                   |                |
+| 8           | YOLOv8x | 36.81 | 21.52 | 51.91 | 39.78 |
+|  |                      |               |                 |                   |                |
+| 9           | YOLOv10l | 35.95 | 21.09 | 52.13 | 38.48 |
+|  |                      |               |                 |                   |                |
+| 10           | YOLOv9m | 36.19 | 20.95 | 51.05 | 39.12 |
+|  |                      |               |                 |                   |                |
+| 11           | YOLOv8m | 34.39 | 19.95 | 48.18 | 38.2 |
+|  |                      |               |                 |                   |                |
+| 12           | YOLOv8s | 31.95 | 18.24 | 45.99 | 35.49 |
+|  |                      |               |                 |                   |                |
+| 13           | YOLOv8n | 28.18 | 15.77 | 40.86 | 31.81 |
+|  |                      |               |                 |                   |                |
+| 14           | YOLOv11n | 27.59 | 15.46 | 39.58 | 31.74 |
+|  |                      |               |                 |                   |                |
+| 15           | YOLOv10n | 27.65 | 15.32 | 41.02 | 31.68 |
+|  |                      |               |                 |                   |                |
+| 16           | YOLOv26n | 26.73 | 14.64 | 38.6 | 31.14 |
+|                |                      |               |                 |                   |                |
+
+---
+
+## Per-Class Performance
+
+| Class                      | mAP@50          | mAP@50-95         |
+| -------------------------- | --------------- | ----------------- |
+|  |                 |                   |
+| pedestrian       | 34.61 | 14.1 |
+|  |                 |                   |
+| people       | 17.67 | 6.19 |
+|  |                 |                   |
+| bicycle       | 14.24 | 6.22 |
+|  |                 |                   |
+| car       | 75.96 | 49.02 |
+|  |                 |                   |
+| van       | 41.02 | 27.83 |
+|  |                 |                   |
+| truck       | 48.34 | 32.22 |
+|  |                 |                   |
+| tricycle       | 23.48 | 12.93 |
+|  |                 |                   |
+| awning-tricycle       | 19.24 | 11.52 |
+|  |                 |                   |
+| bus       | 59.48 | 43.13 |
+|  |                 |                   |
+| motor       | 37.39 | 15.37 |
+|                |                 |                   |
+
+---
+
+## Evaluation Visualizations
+
+### Precision-Recall Curve
+
+![PR Curve](BoxPR_curve.png)
+
+### F1 Curve
+
+![F1 Curve](BoxF1_curve.png)
+
+### Confusion Matrix
+
+![Confusion Matrix](confusion_matrix.png)
+
+---
+
+## Dataset
+
+VisDrone is a large-scale benchmark for object detection in aerial imagery captured from unmanned aerial vehicles (UAVs).
+
+The dataset contains diverse scenes including:
+
+* Urban environments
+* Residential areas
+* Traffic intersections
+* Crowded pedestrian regions
+
+### Classes
+
+* pedestrian
+* people
+* bicycle
+* car
+* van
+* truck
+* tricycle
+* awning-tricycle
+* bus
+* motor
+
+---
+
+## Usage
+
+### Install Dependencies
+
+```bash
+pip install ultralytics huggingface_hub
+```
+
+### Load Model from Hugging Face
+
+```python
+from huggingface_hub import hf_hub_download
+from ultralytics import YOLO
+
+weights = hf_hub_download(
+    repo_id="dronefreak/yolov11l-visdrone",
+    filename="best.pt"
+)
+
+model = YOLO(weights)
+```
+
+### Run Inference
+
+```python
+results = model.predict(
+    source="image.jpg",
+    conf=0.25
+)
+
+results[0].show()
+```
+
+---
+
+## Training Configuration
+
+| Setting          | Value                           |
+| ---------------- | ------------------------------- |
+| Epochs           | 300                             |
+| Dataset          | VisDrone2019-DET                |
+| Framework        | Ultralytics YOLO                |
+| Training Toolkit | VisDrone Dataset Python Toolkit |
+
+---
+
+## Repository Contents
+
+```text
+best.pt
+results.csv
+args.yaml
+BoxPR_curve.png
+BoxF1_curve.png
+confusion_matrix.png
+assets/visdrone_showcase.gif
+README.md
+```
+
+---
+
+## Related Resources
+
+* VisDrone Detection Model Zoo (Hugging Face Collection)
+* VisDrone Dataset Python Toolkit: https://github.com/dronefreak/VisDrone-dataset-python-toolkit
+* VisDrone Dataset: https://github.com/VisDrone/VisDrone-Dataset
+
+---
+
+## Training Framework
+
+These models were trained using the VisDrone Dataset Python Toolkit, an open-source framework for aerial object detection research and benchmarking on the VisDrone dataset.
+
+Features include:
+
+* Dataset preparation and conversion utilities
+* Training and evaluation pipelines
+* Detection benchmarking
+* Visualization tools
+* Support for multiple YOLO model families
+
+Repository:
+
+https://github.com/dronefreak/VisDrone-dataset-python-toolkit
+
+If you find these models useful, please consider starring the repository.
+
+---
+
+## Known Limitations
+
+Performance may degrade in:
+
+* Extremely dense crowds
+* Heavy occlusions
+* Severe motion blur
+* Very small objects occupying only a few pixels
+* Night-time or low-light aerial imagery
+
+---
+
+## Citation
+
+If you use this model in your research, please consider citing:
+
+1. The VisDrone dataset
+2. The original YOLO architecture
+3. The VisDrone Detection Toolkit
+
+```bibtex
+@article{visdrone2019,
+  title={Vision Meets Drones: A Challenge},
+  author={Zhu, Pengfei and Wen, Longyin and Du, Dawei and Bian, Xiao and Ling, Haibin and Hu, Qinghua},
+  journal={International Journal of Computer Vision},
+  year={2021}
+}
+
+@software{Saksena_VisDrone_Detection_Toolkit_2025,
+  author = {Saksena, Saumya Kumaar},
+  title = {VisDrone Detection Toolkit: Modern PyTorch Implementation for Aerial Object Detection},
+  url = {https://github.com/dronefreak/VisDrone-dataset-python-toolkit},
+  version = {2.0.0},
+  year = {2025}
+}
+```

best.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:02588f0bb309ded934641f0fc40b8583a44e6a6bd5929833611a956fab96b4fe
+size 51209177