This directory provides examples and best practices for building image segmentation systems. Our goal is to enable the users to bring their own datasets and train a high-accuracy model easily and quickly.
| Image segmentation example |
|---|
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Our implementation uses fastai's UNet model, where the CNN backbone (e.g. ResNet) is pre-trained on ImageNet and hence can be fine-tuned with only small amounts of annotated training examples. A good understanding of image classification concepts, while not necessary, is strongly recommended.
The following notebooks are provided:
| Notebook name | Description |
|---|---|
| 01_training_introduction.ipynb | Notebook to train and evaluate an image segmentation model. |
| 11_exploring_hyperparameters.ipynb | Finds optimal model parameters using grid search. |
See the contribution guidelines in the root folder.