## Dataset Information
This dataset is related to SEM measurement of the lack of fusion fracture defects in test bars printed using Laser Powder Bed Fusion.
Currently, the dataset consists of 100 annotated images.
#### **Instructions for Generating Predictions and Evaluating Model Performance on the Benchmark Dataset**
- Images and corresponding ground truth masks can be found [here][1].
- For each model, we provide an inference script to generate predictions.
- Refer to the table at the bottom for links to the inference scripts for each model.
- You need to change the paths for images and model in the script accordingly.
- Once you've generated the predictions, you can use the '[get_evaluation_metrics.py][2]' script (applicable to all models in this dataset) to assess the model's performance on the generated predictions.
- You need to change the paths for images in the script accordingly.
**Want to contribute? Fill up this [Google Form][2]**.
<h2>
Benchmarks
</h2>
| Model | Description | Binary IoU | Library | Input Image Size | Performance Metrics | Paper | Model Inference Script | Compatible TensorFlow Version |
|:---------------------------------:|:--------------------------------------------:|:----------:|:--------------------:|:----------------:|:------------------------------------------:|:------------------------------------------:|:------------------------------------------:|:------------------------------------------:|
| [unet.h5][3] | U-Net model with 4-block encoder and decoder | 58%|Tensorflow/Keras | 1024 x 1024| Accuracy <br> Precision <br> Recall <br> Binary IoU* | - | [Click Here][4] | TensorFlow 2.13.0 |
[1]: https://osf.io/rpj4t/files/osfstorage
[2]: https://osf.io/download/6707eda0954e1d03a22ec212/
[3]: https://osf.io/download/acvbz/
[4]: https://osf.io/download/6707eda1755e3e95c6b93f6f/
