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README.md

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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}
+}
+```