Fine-tuned YOLOv11m object detector for aerial imagery using the VisDrone benchmark dataset.
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Fine-tuned YOLOv11m object detector for aerial imagery using the VisDrone benchmark dataset.
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2 extraits--- license: agpl-3.0 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" --- # YOLOv11m Finetuned on VisDrone Fine-tuned YOLOv11m 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 | 36.35 | | mAP@50-95 | 21.02 | | Precision | 50.24 | | Recall | 39.46 | | F1 Score | 44.2 | | Parameters | 20.1M | | FLOPs | 68.5B | --- ## 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 | YOLOv26m | 36.67 | 21.22 | 51.03 | 39.79 | | | | | | | | | 10 | YOLOv10l | 35.95 | 21.09 | 52.13 | 38.48 | | | | | | | | | 11 | YOLOv11m | 36.35 | 21.02 | 50.24 | 39.46 | | | | | | | | | 12 | YOLOv9m | 36.19 | 20.95 | 51.05 | 39.12 | | | | | | | | | 13 | YOLOv8m | 34.39 | 19.95 | 48.18 | 38.2 | | | | | | | | | 14 | YOLOv9s | 33.52 | 19.26 | 46.16 | 37.43 | | | | | | | | | 15 | YOLOv11s | 32.3 | 18.47 | 45.49 | 35.31 | | | | | | | | | 16 | YOLOv8s | 31.95 | 18.24 | 45.99 | 35.49 | | | | | | | | | 17 | YOLOv26s | 32.1 | 18.06 | 45.75 | 35.05 | |...
Source context: 154 downloads · 4 likes · Pipeline object-detection · Library ultralytics · Repo dronefreak/visdrone-yolov11m