Trashscan8s
YOLOv8 mobilemodelpackageyolov8sv2 ONNX model for waste classification.
Fuente del modelo
- Tipo
- Other
- Tamaño del archivo
- 42,7 MB
- Versión
- vRevision b424bc9
- Actualizado
- 16 sept 2026
- Descargas
- 0
- Fuente
- Hugging Face
Descripción de la fuente
trashscan8s
YOLOv8 mobile_model_package_yolov8s_v2 ONNX model for waste classification.
Classes
| Index | Class |
|---|---|
| 0 | paper |
| 1 | plastic |
| 2 | metal |
| 3 | organic |
| 4 | other |
Usage
import onnxruntime
import numpy as np
from PIL import Image
session = onnxruntime.InferenceSession("best.onnx")
input_name = session.get_inputs()[0].name
# Input: [1, 3, 640, 640] normalized float32
# Output: [1, 9, 8400] = [cx, cy, w, h, p0, p1, p2, p3, p4]
Model Specs
- Input size: 640x640
- Format: ONNX
- Confidence threshold: 0.25
- IoU threshold: 0.5
Fuentes
1 fuente
Fuentes
1 fuenteVerificado 2 sept
Artefactos del modelo
1 artefacto
Artefactos del modelo
1 artefactoExtractos de fuentes
2 extractos
Extractos de fuentes
2 extractos--- language: en license: mit tags: - yolov8 - object-detection - waste-classification library_name: onnx pipeline_tag: object-detection --- # trashscan8s YOLOv8 mobile_model_package_yolov8s_v2 ONNX model for waste classification. ## Classes | Index | Class | |-------|-------| | 0 | paper | | 1 | plastic | | 2 | metal | | 3 | organic | | 4 | other | ## Usage ```python import onnxruntime import numpy as np from PIL import Image session = onnxruntime.InferenceSession("best.onnx") input_name = session.get_inputs()[0].name # Input: [1, 3, 640, 640] normalized float32 # Output: [1, 9, 8400] = [cx, cy, w, h, p0, p1, p2, p3, p4] ``` ## Model Specs - Input size: 640x640 - Format: ONNX - Confidence threshold: 0.25 - IoU threshold: 0.5
Source context: 0 downloads · 0 likes · Pipeline object-detection · Library onnx · Repo ditoow/trashscan8s
