Instructions to use datasidahmed/YOLOV8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use datasidahmed/YOLOV8 with ultralytics:
from ultralytics import YOLOvv8 model = YOLOvv8.from_pretrained("datasidahmed/YOLOV8") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Add metrics summary JSON
Browse files- metrics_summary.json +44 -0
metrics_summary.json
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{
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"model": {
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"architecture": "YOLOv8n",
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"parameters": 3013188,
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"gflops": 8.2,
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"file_size_mb": 24.5,
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"task": "object-detection",
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"input_size": [640, 640],
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"framework": "Ultralytics 8.x",
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"num_classes": 12
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},
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"classes": {
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"0": "camouflage_soldier",
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"1": "weapon",
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"2": "military_tank",
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"3": "military_truck",
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"4": "military_vehicle",
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"5": "civilian",
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"6": "soldier",
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"7": "civilian_vehicle",
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"8": "military_artillery",
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"9": "trench",
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"10": "military_aircraft",
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"11": "military_warship"
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},
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"training": {
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"base_model": "yolov8n.pt",
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"epochs": 100,
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"image_size": 640,
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"batch_size": 16,
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"optimizer": "AdamW",
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"conf_threshold": 0.40,
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"iou_threshold": 0.50
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},
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"validation_metrics": {
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"mAP50": 0.72,
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"mAP50_95": 0.48,
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"precision": 0.74,
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"recall": 0.68,
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"inference_speed_cpu_ms": 120
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},
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"repository": "https://huggingface.co/datasidahmed/YOLOV8",
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"author": "Sidahmed Melainin"
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}
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