This is project D: Severstal Steel Defect Detection using semantic segmentation by team 3Nature_2Journal_8GPU.
The dataset for this project can be found here. To run with our default path setup, create a new folder called "inputs" in the same directory as the one containing this cloned respository. Download and extract the dataset inside "inputs." The kaggle submission kernel can be found here. It contains the code to upload submission.csv files and submit to Kaggle. Inside the kernel, load the dataset called ece285_submissions
to access all generated submission files. The submissions can be directly downloaded from kaggle here.
All the package requirements can be found in requirements.txt. To install the requirements:
git clone https://github.com/ucrajkumar/ece285
cd ece285
pip install -r requirements.txt
If working in a conda environment, use:
conda install --file requirements.txt
The required file structure to run with our default paths are as follows: File Structure
- Current Directory
- Inputs
- Downloaded data from Kaggle
- ece285 (this repo)
- Inputs
file name | Description of file |
---|---|
Demo.ipynb | Qualitatively evaluate all models on validation and test set images |
Figures.ipynb | Recreate figures from report |
Train.ipynb | Train u-net, u-net with residual encoder, and u-net with inverted residual encoder |
deeplabv3.ipynb | Train deeplabv3+ |
Inference.ipynb | Predict on test set and export results |
data_gen.py | Data generator for loading train, validation, and test images |
model.py | Code for building all 4 models |
utils.py | Accuracy and loss metrics, conversion from mask to RLE and vice-versa, and post-processing |
train_idx.npy | indices for training set |
val_idx.npy | indices for validation set |
folder name | Description of file |
---|---|
ex_images | Example validation and test images for Demo.ipynb |
history | Training history for each model |
models | Trained model files |
submissions | Exported results on test set |
*Note: The submission for deeplab was too large to upload onto github. However, it is in ece285_submissions
folder in Kaggle.