Spaces:
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| title: Defectdetection | |
| emoji: 🚀 | |
| colorFrom: blue | |
| colorTo: yellow | |
| sdk: streamlit | |
| sdk_version: 1.27.2 | |
| app_file: app06.py | |
| pinned: false | |
| license: mit | |
| # 🛠️ PCB Defect Detection App | |
| This app allows users to upload PCB images and detect defects using state-of-the-art machine learning models. | |
| ## 🌟 Features | |
| - **Image Upload**: Easily upload your PCB images and get instant defect predictions. | |
| - **Visualization**: Visualize the detected defects on the PCB image. | |
| - **Defect Types**: The app can identify multiple types of defects and highlight them uniquely for easy identification. | |
| ## 🚀 Usage | |
| ### 1️⃣ Uploading an Image: | |
| - Click on the "Browse files" button. | |
| - Select a PCB image from your device. | |
| - Sit back and relax! Let the model churn through the image and present its findings. | |
| ### 2️⃣ Interpreting Results: | |
| - It will display the original image alongside the predicted defect mask. | |
| - Different defect types will be highlighted using unique grayscale values. | |
| ## Model Details | |
| The app utilzes the Segformer model trained on a custom PCB dataset. The model has been fine-tuned to detect: | |
| - **Incorrect Installation** | |
| - **Short Circuit** | |
| - **Dry Joints** | |
| ... commonly found defects in PCBs. | |
| ## 📜 Requirements | |
| The app is built using `Streamlit` and leverages the `Hugging Face Transformers` library for model inference. For a full list of requirements, refer to the `requirements.txt` file. | |
| Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference | |