File size: 2,345 Bytes
f88a971 33401aa f88a971 33401aa f88a971 33401aa f88a971 33401aa f88a971 80e61f2 f88a971 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 |
---
language:
- en
tags:
- defect-detection
- image-classification
- machine-learning
- quality-control
- ensemble-learning
- neural-networks
license: apache-2.0
datasets:
- custom_paper_surface_defect
pipeline_tag: image-classification
model-index:
- name: Paper Defect Detection
results:
- task:
type: image-classification
name: Surface Defect Detection
metrics:
- type: accuracy
value: 0.81
name: Ensemble Test Accuracy
- type: f1
value: 0.8
name: F1 Score
library_name: sklearn
---
# Paper Defect Detection
## Model Description
This model is designed for automated surface defect detection in manufacturing using a hybrid approach that combines classical machine learning and deep learning techniques.
### Model Architecture
The model uses a hybrid architecture combining:
- Logistic Regression
- SVM
- Naive Bayes
- CNN
- Ensemble Voting Classifier
### Feature Extraction Methods
- Histogram of Oriented Gradients (HOG)
- Gabor Filters
- Canny Edge Detection
- Wavelet Transforms
## Performance
| Model | Train Accuracy | Test Accuracy |
|--------------------|----------------|---------------|
| Logistic Regression| 0.99 | 0.79 |
| SVM | 0.86 | 0.80 |
| Ensemble Model | 0.90 | 0.81 |
## Limitations
- Performance may degrade for defect types not represented in the training data
- Variations in lighting or textures can affect classification accuracy
- This was a university project with room for improvement
## Usage
```python
from transformers import AutoModelForImageClassification, AutoFeatureExtractor
import torch
from PIL import Image
from torchvision import transforms
model_name = "your-username/surface-defect-detection"
model = AutoModelForImageClassification.from_pretrained(model_name)
feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
# Preprocess the input image
transform = transforms.Compose([
transforms.Resize((128, 128)),
transforms.ToTensor()
])
image = Image.open("path/to/sample-image.jpg")
inputs = feature_extractor(images=image, return_tensors="pt")
# Perform inference
with torch.no_grad():
outputs = model(**inputs)
predicted_class = outputs.logits.argmax(-1).item()
print(f"Predicted Defect Class: {predicted_class}")
``` |