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_exp018.yaml with huggingface_hub
Browse files- mira_exp018.yaml +20 -0
mira_exp018.yaml
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name: EXP-018 YOLO11n Teacher
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display_name: MIRA EXP-018 YOLO11n Teacher
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class_names:
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- glass
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- metal
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- paper
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- plastic
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- trash
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model_type: yolo_pt
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model_file: mira_exp018.pt
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metrics:
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mAP50: 0.906
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mAP50_95: 0.822
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tags:
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- teacher
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- balanced
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- no_sortwaste
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- kaggle_t4
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- adamw
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description: YOLO11n teacher trained on balanced 4-source dataset (dmedhi+TACO+Roboflow+TrashNet, 5108 train/415 val). Achieves 90.6% mAP50 on tabletop TrashNet validation. INT8 TFLite export 2.90 MB.
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