This repo contains two files: "image_crop.py" is used to remove the black bar at the bottom of the micrograph. The jupyter notebook is the main file doing analysis.
In the notebook, the following tasks were performed:
- Preprocess the dataset and split it into train and test parts.
- Extract features from different intermediate layers and compare their performance.
- Use the highest accurate layer to obtain features to train a group of binary SVM classifiers.
- Use voting scheme to obtain a multi-class classifier.
spheroidite network | spheroidite pearlite | spheroidite Widmanstatten | network pearlite | network Widmanstatten | pearlite Widmanstatten | |
---|---|---|---|---|---|---|
spheroidite | 0.00729927 | 0.0036496 | 0.13138686 | |||
network | 0.10714 | 0.080357 | 0.0535714 | |||
pearlite | 0.08333 | 0 | 0 | |||
Widmanstatten | 0.380952 | 0 | 0.285714286 |
Multi-label voting classifiers | |
---|---|
spheroidite | 0.142335766 |
network | 0.107142857 |
pearlite | 0.083333 |
Widmanstatten | 0.380952381 |
Source of dataset: http://uhcsdb.materials.cmu.edu/