Image Classification
ultralytics
TensorBoard
English
Tyre Quality
Tyre Classification
Image Classification
Machine Learning
Pytorch
Deep Learning
Computer Vision
Prediction
yolov8
yolo
TyreInspection
QualityControl
DefectDetection
AutomotiveAI
SafetyStandards
IndustrialAI
AIQualityAssessment
PredictiveMaintenance
AIModel
Eval Results (legacy)
Instructions to use foduucom/Tyre-Quality-Classification-AI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use foduucom/Tyre-Quality-Classification-AI with ultralytics:
from ultralytics import YOLOvv8 model = YOLOvv8.from_pretrained("foduucom/Tyre-Quality-Classification-AI") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Commit ·
74e99f1
1
Parent(s): 135ddb4
Update README.md
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README.md
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@@ -97,6 +97,8 @@ pip install ultralyticsplus==0.0.28 ultralytics==8.0.43
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```python
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from ultralyticsplus import YOLO, render_result
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# load model
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model = YOLO('foduucom/Tyre-Quality-Classification-AI')
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model.overrides['max_det'] = 1000 # maximum number of detections per image
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# set image
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image = '/
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# perform inference
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results = model
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# observe results
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top_class_index = results[0].probs.
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Class = model.names[top_class_index]
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print(Class)
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```python
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from ultralyticsplus import YOLO, render_result
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import torch
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# load model
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model = YOLO('foduucom/Tyre-Quality-Classification-AI')
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model.overrides['max_det'] = 1000 # maximum number of detections per image
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# set image
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image = '/content/90-100-10-ceat-500x500.jpeg'
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# perform inference
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results = model(image)
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# observe results
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top_class_index = torch.argmax(results[0].probs).item()
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Class = model.names[top_class_index]
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print(Class)
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