smart-fridge-yolo11n

Smart Fridge YOLO11n

这是一个面向智能冰箱场景训练的 YOLO11n 食材目标检测模型。模型用于边缘端预识别、区域定位、入库变化提醒和重复候选标记；食物名称确认、可见状态分析及综合建议由后续 VLM 和规则系统处理。

This YOLO11n detector is trained for smart-fridge food pre-detection. It is intended for object localization, inventory-change triggers, and duplicate-candidate marking on edge devices. Semantic confirmation and visible-condition assessment are delegated to a downstream VLM and rule layer.

Files

Training Data

• Dataset: fridge food images v14
• Source: Roboflow Universe
• Images reported by the exported dataset: 5,139
• License reported by the dataset export: CC BY 4.0
• Classes: 30 food categories, listed in classes.txt

The class labels preserve the spelling used by the source dataset for compatibility with the deployed pipeline.

Training

Validation Results

Metrics come from the validation split configured in the exported dataset.

Usage

from ultralytics import YOLO

model = YOLO("best.pt")
results = model.predict("fridge.jpg", imgsz=640, conf=0.65)

The ONNX file is intended for CPU inference with ONNX Runtime. The deployed smart-fridge pipeline uses a confidence threshold of 0.65 before triggering downstream VLM analysis.

Limitations

• The model only detects the 30 classes in classes.txt and may not generalize to unseen foods, packaging, lighting, occlusion, or camera angles.
• A detection is not proof of identity, freshness, edibility, expiry, or food safety.
• The model was trained primarily on a public image dataset and requires evaluation or adaptation for each physical refrigerator.
• Dataset class names include source spelling such as blue berry and stawberry; applications may map them to corrected display names without changing output indices.
• This model should be used as a pre-detection component, with human review or downstream multimodal analysis for consequential decisions.

License

Ultralytics states that models trained with its YOLO training stack are covered by AGPL-3.0 by default. Commercial or proprietary deployment may require an Ultralytics Enterprise license. The training dataset is separately attributed as CC BY 4.0 by its Roboflow export; users are responsible for complying with both the model and dataset terms.