Trashscan8m
YOLOv8 mobilemodelpackageyolov8mv2 ONNX model for waste classification.
Source du modèle
- Type
- Other
- Taille du fichier
- 98,8 MB
- Version
- vRevision c76b2b8
- Mis à jour
- 16 sept. 2026
- Téléchargements
- 0
- Source
- Hugging Face
Description de la source
trashscan8m
YOLOv8 mobile_model_package_yolov8m_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
Sources
1 source
Sources
1 sourceVérifié 2 sept.
Artefacts du modèle
1 artefact
Artefacts du modèle
1 artefactExtraits de sources
2 extraits
Extraits de sources
2 extraits--- language: en license: mit tags: - yolov8 - object-detection - waste-classification library_name: onnx pipeline_tag: object-detection --- # trashscan8m YOLOv8 mobile_model_package_yolov8m_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/trashscan8m
