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README.md

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+---
+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/YOLOv9"
+---
+
+
+# YOLOv9m Finetuned on VisDrone
+
+Fine-tuned YOLOv9m 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     | 36.19     |
+| mAP@50-95  | 20.95   |
+| Precision  | 51.05 |
+| Recall     | 39.12    |
+| F1 Score   | 44.3        |
+| 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       | 31.55 | 12.7 |
+|  |                 |                   |
+| people       | 17.29 | 5.86 |
+|  |                 |                   |
+| bicycle       | 14.21 | 6.0 |
+|  |                 |                   |
+| car       | 75.48 | 48.36 |
+|  |                 |                   |
+| van       | 40.72 | 27.28 |
+|  |                 |                   |
+| truck       | 45.28 | 29.8 |
+|  |                 |                   |
+| tricycle       | 23.65 | 12.25 |
+|  |                 |                   |
+| awning-tricycle       | 19.51 | 11.21 |
+|  |                 |                   |
+| bus       | 58.4 | 41.77 |
+|  |                 |                   |
+| motor       | 35.83 | 14.29 |
+|                |                 |                   |
+
+---
+
+## 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/yolov9m-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}
+}
+```

args.yaml

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+task: detect
+mode: train
+model: yolov9m.pt
+data: /tmp/visdrone_yolo_taxb635w/dataset.yaml
+epochs: 300
+time: null
+patience: 100
+batch: 16
+imgsz: 640
+save: true
+save_period: -1
+cache: false
+device: '0'
+workers: 4
+project: /home/saumya.saksena/projects/VisDrone-dataset-python-toolkit/outputs/yolov9m_300ep
+name: yolov9m
+exist_ok: true
+pretrained: true
+optimizer: auto
+verbose: true
+seed: 0
+deterministic: true
+single_cls: false
+rect: false
+cos_lr: false
+close_mosaic: 10
+resume: false
+amp: true
+fraction: 1.0
+profile: false
+freeze: null
+multi_scale: 0.0
+compile: false
+overlap_mask: true
+mask_ratio: 4
+dropout: 0.0
+val: true
+split: val
+save_json: false
+conf: null
+iou: 0.7
+max_det: 300
+half: false
+dnn: false
+plots: true
+end2end: null
+source: null
+vid_stride: 1
+stream_buffer: false
+visualize: false
+augment: false
+agnostic_nms: false
+classes: null
+retina_masks: false
+embed: null
+show: false
+save_frames: false
+save_txt: false
+save_conf: false
+save_crop: false
+show_labels: true
+show_conf: true
+show_boxes: true
+line_width: null
+format: torchscript
+keras: false
+optimize: false
+int8: false
+dynamic: false
+simplify: true
+opset: null
+workspace: null
+nms: false
+lr0: 0.005
+lrf: 0.01
+momentum: 0.937
+weight_decay: 0.0005
+warmup_epochs: 3.0
+warmup_momentum: 0.8
+warmup_bias_lr: 0.1
+box: 7.5
+cls: 0.5
+cls_pw: 0.0
+dfl: 1.5
+pose: 12.0
+kobj: 1.0
+rle: 1.0
+angle: 1.0
+nbs: 64
+hsv_h: 0.015
+hsv_s: 0.7
+hsv_v: 0.4
+degrees: 0.0
+translate: 0.1
+scale: 0.5
+shear: 0.0
+perspective: 0.0
+flipud: 0.0
+fliplr: 0.5
+bgr: 0.0
+mosaic: 1.0
+mixup: 0.0
+cutmix: 0.0
+copy_paste: 0.0
+copy_paste_mode: flip
+auto_augment: randaugment
+erasing: 0.4
+cfg: null
+tracker: botsort.yaml
+save_dir: /home/saumya.saksena/projects/VisDrone-dataset-python-toolkit/outputs/yolov9m_300ep/yolov9m

best.pt

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