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Snapstock Fruit Vegetable Detector

This repository stores a YOLOv8m fruit and vegetable object-detection checkpoint used as a baseline model for the SnapStock-AI university project.

Fuentehuggingface.co/Senu-12/snapstock-fruit-vegetable-detector/tree/398e0981f777f683aead90144b7d53cd22a4fbf4
TipoOtherVersiónRevision 398e098Bcalidad

Fuente del modelo

Tipo
Other
Tamaño del archivo
49,7 MB
Versión
vRevision 398e098
Actualizado
21 oct 2026
Descargas
155
Fuente
Hugging Face

Descripción de la fuente

SnapStock-AI Fruit and Vegetable Detector

This repository stores a YOLOv8m fruit and vegetable object-detection checkpoint used as a baseline model for the SnapStock-AI university project.

Original source

The checkpoint was obtained from the baseline models published with:

  • Original author: Henning Heyen
  • Original project: Fruits-And-Vegetables-Detection-Dataset
  • Dataset: LVIS Fruits and Vegetables
  • Classes: 63
  • Architecture: YOLOv8m

This repository does not claim authorship of the original dataset or baseline model. The checkpoint is mirrored here to provide stable model downloading for the SnapStock-AI development team.

Intended use

The model is used to:

  • Detect fruits and vegetables
  • Return bounding boxes
  • Identify object classes
  • Count detected items

Limitations

The original project reports class imbalance and duplicate Tomato/tomato and Strawberry/strawberry categories. Performance should be tested using real SnapStock-AI inventory images before production use.

Local loading

from ultralytics import YOLO

model = YOLO("fruit_vegetable_yolov8m.pt")
results = model.predict("inventory-image.jpg")

Fuentes

1 fuente
Hugging FacePrincipalVersión 398e0981f777f683aead90144b7d53cd22a4fbf4
huggingface.co/Senu-12/snapstock-fruit-vegetable-detector/tree/398e0981f777f683aead90144b7d53cd22a4fbf4

Verificado 21 oct

Artefactos del modelo

1 artefacto

yolov8/fruit_vegetable_yolov8m.pt

pt · 49,7 MB · SHA-256 a18624d446e2…e5d3 · Hugging Face

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Extractos de fuentes

3 extractos
Readme5 agohuggingface.co/Senu-12/snapstock-fruit-vegetable-detector

--- library_name: ultralytics pipeline_tag: object-detection tags: - yolov8 - object-detection - fruits - vegetables - snapstock-ai license: other --- # SnapStock-AI Fruit and Vegetable Detector This repository stores a YOLOv8m fruit and vegetable object-detection checkpoint used as a baseline model for the SnapStock-AI university project. ## Original source The checkpoint was obtained from the baseline models published with: - Original author: Henning Heyen - Original project: Fruits-And-Vegetables-Detection-Dataset - Dataset: LVIS Fruits and Vegetables - Classes: 63 - Architecture: YOLOv8m This repository does not claim authorship of the original dataset or baseline model. The checkpoint is mirrored here to provide stable model downloading for the SnapStock-AI development team. ## Intended use The model is used to: - Detect fruits and vegetables - Return bounding boxes - Identify object classes - Count detected items ## Limitations The original project reports class imbalance and duplicate Tomato/tomato and Strawberry/strawberry categories. Performance should be tested using real SnapStock-AI inventory images before production use. ## Local loading ```python from ultralytics import YOLO model = YOLO("fruit_vegetable_yolov8m.pt") results = model.predict("inventory-image.jpg")

Note21 octhuggingface.co/Senu-12/snapstock-fruit-vegetable-detector

Source context: 155 downloads · 0 likes · Pipeline object-detection · Library ultralytics · Repo Senu-12/snapstock-fruit-vegetable-detector

Note5 agohuggingface.co/Senu-12/snapstock-fruit-vegetable-detector

Source context: 69 downloads · 0 likes · Pipeline object-detection · Library ultralytics · Repo Senu-12/snapstock-fruit-vegetable-detector