A tensorflow implementation of the paper "Searching for MobileNetV3" with a R-ASPP segmenter head and classification head.
This project is in active development and as such, has a lot of rough edges. The implementation should be true to the original paper, but your mileage may vary.
pip install -r requirements.txt
The implementation was initially done to apply the MobileNet v3 architecture to skin lesion segmentation, as per the ISIC Challenge 2018. The 2018 edition was chosen as it is the last one with a segmentation task.
For segmentation, two metrics are availables, namely Dice Coefficient and Jaccard Index. In both cases a matching loss is also provided.
The original paper used RMSProp, but we found out that training could be sped up by using Adam instead.
You can download the files here.
Once that's done, create this hierarchy in your directories:
You should be able to run it with python3 train.py
You can download the dataset here.
TODO
To get predictions for one image
Run python3 eval.py --model-path out/ -i your_image.png to output the segmentation mask.
To get predictions for multiple images
TODO
To get predictions for one image
TODO
To get predictions for multiple images
Run python3 eval_classification.py --model-path out/your_model.h5 --input-dir your_test_dir/ --labels-file labels.csv to output the confusion matrix.