README.md

---
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}
}
```