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---
tags:
- image-classification
- defect-detection
- quality-control
- pytorch
- vision
library_name: pytorch
pipeline_tag: image-classification
---
# RKLB Component Defect Detection Model
## Model Description
This model is designed for automated quality control in manufacturing, specifically for detecting defects in components.
- **Task**: Binary Image Classification (Normal vs Defective)
- **Architecture**: efficient_vit
- **Input Size**: 224x224 RGB images
- **Classes**: Normal, Defective
- **Accuracy**: 97.5%
## Usage
### With the RKLB Defect Detection Space
The easiest way to use this model is through the [RKLB Materials Space](https://huggingface.co/spaces/gphua1/rklb_materials).
### Programmatic Usage
```python
from huggingface_hub import hf_hub_download
import torch
# Download model
model_path = hf_hub_download(
repo_id="gphua1/rklb-defect-model",
filename="best_model.pth"
)
# Load model
checkpoint = torch.load(model_path, map_location='cpu')
# ... initialize your model architecture and load weights
```
## Training Details
- Framework: PyTorch
- Model Type: Vision Transformer (ViT) variant
- Training Data: Manufacturing component images
- Task: Binary classification for quality control
## Intended Use
This model is intended for:
- Automated quality inspection in manufacturing
- Component defect detection
- Production line quality control
- Training data augmentation for quality systems
## Limitations
- Designed for specific component types
- Best performance on similar lighting conditions as training data
- Binary classification only (normal/defective)
## Citation
If you use this model, please cite:
```
@misc{rklb-defect-model,
author = {Gary Phua},
title = {RKLB Component Defect Detection Model},
year = {2024},
publisher = {HuggingFace},
url = {https://huggingface.co/gphua1/rklb-defect-model}
}
```
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