Object Detection
ultralytics
LiteRT
Keras
ONNX
English
yolo
yolo11
yolo11n
yolov11
yolov11n
computer-vision
waste-detection
trash-detection
garbage-detection
recycling
recycling-automation
waste-sorting
edge-ai
Instructions to use Jeremy341/MIRA-AI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use Jeremy341/MIRA-AI with ultralytics:
from ultralytics import YOLOvv11 model = YOLOvv11.from_pretrained("Jeremy341/MIRA-AI") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Upload mira_exp014.yaml with huggingface_hub
Browse files- mira_exp014.yaml +14 -0
mira_exp014.yaml
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name: mira_exp014
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display_name: \"EXP-014 (YOLO11n, TACO+TrashNet+Roboflow)\"
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model_type: yolo_pt
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task: detect
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input_size: 640
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class_names: [\"glass\", \"metal\", \"paper\", \"plastic\", \"trash\"]
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training:
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dataset: \"TACO + TrashNet + Roboflow\"
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epochs: 120
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metrics:
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map50: 0.607
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map50_95: 0.506
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tags: [\"yolo11n\", \"multidataset\", \"best-so-far\"]
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deployment_status: pending_target_hardware_validation
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