Hugging Face's logo

23smartfactory
/
vehicle-assembly-process-defect-detection-model

Image Classification
PyTorch
Korean
resnet
defect-detection
manufacturing
quality-control
Model card Files
xet
Community
1
vehicle-assembly-process-defect-detection-model
File size: 4,205 Bytes 
8184aec
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
 
---
license: cc-by-nc-4.0
tags:
- image-classification
- pytorch
- defect-detection
- manufacturing
- quality-control
language:
- ko
datasets:
- custom
metrics:
- accuracy
library_name: pytorch
pipeline_tag: image-classification
---

# 의μž₯곡정 λΆˆλŸ‰ν’ˆ λΆ„λ₯˜ λͺ¨λΈ (Assembly Process Defect Classification)

이 λͺ¨λΈμ€ 의μž₯κ³΅μ •μ—μ„œ λ°œμƒν•˜λŠ” λ‹€μ–‘ν•œ λΆˆλŸ‰ μœ ν˜•μ„ λΆ„λ₯˜ν•˜κΈ° μœ„ν•΄ ResNet50 μ•„ν‚€ν…μ²˜λ₯Ό 기반으둜 νŒŒμΈνŠœλ‹λœ λͺ¨λΈμž…λ‹ˆλ‹€.

## λͺ¨λΈ 정보

- **μ•„ν‚€ν…μ²˜**: ResNet50
- **클래슀 수**: 24개
- **μž…λ ₯ 크기**: 224x224 RGB 이미지
- **λΆ„λ₯˜ μΉ΄ν…Œκ³ λ¦¬**: 12가지 λΆˆλŸ‰ μœ ν˜• Γ— 2가지 ν’ˆμ§ˆ μƒνƒœ (λΆˆλŸ‰ν’ˆ/μ–‘ν’ˆ)

## λΆ„λ₯˜ 클래슀

### λΆˆλŸ‰ μœ ν˜•λ³„ λΆ„λ₯˜
- **κ³ μ • λΆˆλŸ‰**: λΆˆλŸ‰ν’ˆ(0), μ–‘ν’ˆ(1)
- **κ³ μ •ν•€ λΆˆλŸ‰**: λΆˆλŸ‰ν’ˆ(2), μ–‘ν’ˆ(3)
- **단차**: λΆˆλŸ‰ν’ˆ(4), μ–‘ν’ˆ(5)
- **슀크래치**: λΆˆλŸ‰ν’ˆ(6), μ–‘ν’ˆ(7)
- **싀링 λΆˆλŸ‰**: λΆˆλŸ‰ν’ˆ(8), μ–‘ν’ˆ(9)
- **연계 λΆˆλŸ‰**: λΆˆλŸ‰ν’ˆ(10), μ–‘ν’ˆ(11)
- **μ™Έκ΄€ 손상**: λΆˆλŸ‰ν’ˆ(12), μ–‘ν’ˆ(13)
- **유격 λΆˆλŸ‰**: λΆˆλŸ‰ν’ˆ(14), μ–‘ν’ˆ(15)
- **μž₯μ°© λΆˆλŸ‰**: λΆˆλŸ‰ν’ˆ(16), μ–‘ν’ˆ(17)
- **체결 λΆˆλŸ‰**: λΆˆλŸ‰ν’ˆ(18), μ–‘ν’ˆ(19)
- **헀밍 λΆˆλŸ‰**: λΆˆλŸ‰ν’ˆ(20), μ–‘ν’ˆ(21)
- **홀 λ³€ν˜•**: λΆˆλŸ‰ν’ˆ(22), μ–‘ν’ˆ(23)

## μ‚¬μš©λ²•

### λͺ¨λΈ λ‘œλ“œ 및 μΆ”λ‘ 

```python
import torch
from torchvision import models, transforms
from PIL import Image

# λͺ¨λΈ λ‘œλ“œ
model = models.resnet50(num_classes=24)
model.fc = torch.nn.Linear(model.fc.in_features, 24)
model.load_state_dict(torch.load('pytorch_model.bin', map_location='cpu'))
model.eval()

# 이미지 μ „μ²˜λ¦¬
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406],
                         std=[0.229, 0.224, 0.225])
])

# μΆ”λ‘ 
img = Image.open('your_image.jpg').convert('RGB')
input_tensor = transform(img).unsqueeze(0)

with torch.no_grad():
    outputs = model(input_tensor)
    predicted_class = torch.argmax(outputs, dim=1).item()

# 클래슀λͺ… 맀핑
class_names = {
    0: 'κ³ μ • λΆˆλŸ‰_λΆˆλŸ‰ν’ˆ', 1: 'κ³ μ • λΆˆλŸ‰_μ–‘ν’ˆ',
    2: 'κ³ μ •ν•€ λΆˆλŸ‰_λΆˆλŸ‰ν’ˆ', 3: 'κ³ μ •ν•€ λΆˆλŸ‰_μ–‘ν’ˆ',
    4: '단차_λΆˆλŸ‰ν’ˆ', 5: '단차_μ–‘ν’ˆ',
    6: '슀크래치_λΆˆλŸ‰ν’ˆ', 7: '슀크래치_μ–‘ν’ˆ',
    8: '싀링 λΆˆλŸ‰_λΆˆλŸ‰ν’ˆ', 9: '싀링 λΆˆλŸ‰_μ–‘ν’ˆ',
    10: '연계 λΆˆλŸ‰_λΆˆλŸ‰ν’ˆ', 11: '연계 λΆˆλŸ‰_μ–‘ν’ˆ',
    12: 'μ™Έκ΄€ 손상_λΆˆλŸ‰ν’ˆ', 13: 'μ™Έκ΄€ 손상_μ–‘ν’ˆ',
    14: '유격 λΆˆλŸ‰_λΆˆλŸ‰ν’ˆ', 15: '유격 λΆˆλŸ‰_μ–‘ν’ˆ',
    16: 'μž₯μ°© λΆˆλŸ‰_λΆˆλŸ‰ν’ˆ', 17: 'μž₯μ°© λΆˆλŸ‰_μ–‘ν’ˆ',
    18: '체결 λΆˆλŸ‰_λΆˆλŸ‰ν’ˆ', 19: '체결 λΆˆλŸ‰_μ–‘ν’ˆ',
    20: '헀밍 λΆˆλŸ‰_λΆˆλŸ‰ν’ˆ', 21: '헀밍 λΆˆλŸ‰_μ–‘ν’ˆ',
    22: '홀 λ³€ν˜•_λΆˆλŸ‰ν’ˆ', 23: '홀 λ³€ν˜•_μ–‘ν’ˆ'
}

print(f"예츑 결과: {class_names[predicted_class]}")
```

### ν—ˆκΉ…νŽ˜μ΄μŠ€ Transformers 라이브러리 μ‚¬μš©

```python
from transformers import AutoConfig
import torch
from torchvision import models

# μ„€μ • λ‘œλ“œ
config = AutoConfig.from_pretrained('your-username/defect-classification-resnet50')

# λͺ¨λΈ λ‘œλ“œ
model = models.resnet50(num_classes=config.num_classes)
model.fc = torch.nn.Linear(model.fc.in_features, config.num_classes)
model.load_state_dict(torch.hub.load_state_dict_from_url(
    'https://huggingface.co/your-username/defect-classification-resnet50/resolve/main/pytorch_model.bin',
    map_location='cpu'
))
```

## λͺ¨λΈ μ„±λŠ₯

- **정확도**: 0.7509
- **검증 데이터셋**: [데이터셋 정보 μž…λ ₯]

## μ œν•œμ‚¬ν•­

- 이 λͺ¨λΈμ€ νŠΉμ • 제쑰 ν™˜κ²½μ—μ„œ μˆ˜μ§‘λœ λ°μ΄ν„°λ‘œ ν•™μŠ΅λ˜μ—ˆμœΌλ―€λ‘œ, λ‹€λ₯Έ ν™˜κ²½μ—μ„œλŠ” μ„±λŠ₯이 λ‹¬λΌμ§ˆ 수 μžˆμŠ΅λ‹ˆλ‹€.
- μ‹€μ œ 운영 ν™˜κ²½μ—μ„œ μ‚¬μš©ν•˜κΈ° 전에 μΆ©λΆ„ν•œ ν…ŒμŠ€νŠΈλ₯Ό ꢌμž₯ν•©λ‹ˆλ‹€.

## λΌμ΄μ„ μŠ€

CC BY-NC

## 인용

이 λͺ¨λΈμ„ μ‚¬μš©ν•˜μ‹ λ‹€λ©΄ λ‹€μŒκ³Ό 같이 μΈμš©ν•΄μ£Όμ„Έμš”:

```
@misc{vehicle-assembly-process-defect-detection-model,
  title={Assembly Process Defect Classification with ResNet50},
  author={doyoon kwon},
  year={2025},
  url={https://huggingface.co/23smartfactory/vehicle-assembly-process-defect-detection-model}
}
```