Custom semantic segmentation tutorial #2588
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| "- `configure_callbacks`: We demonstrate how to stack `ModelCheckpoint` callbacks to save the best checkpoint as well as periodic checkpoints\n", | ||
| "- `on_train_epoch_start`: We log the learning rate at the start of each epoch so we can easily see how it decays over a training run\n", | ||
| "\n", | ||
| "Overall these demonstrate how to customize the training routine to investigate specific research questions (e.g., of the scheduler on test performance)." |
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| "Overall these demonstrate how to customize the training routine to investigate specific research questions (e.g., of the scheduler on test performance)." | |
| "Overall these demonstrate how to customize the training routine to investigate specific research questions (e.g., the effect of the scheduler on test performance)." |
| "source": [ | ||
| "## Train model\n", | ||
| "\n", | ||
| "The remainder of the turial is straightforward and follows the typical [PyTorch Lightning](https://lightning.ai/) training routine. We instantiate a `DataModule` for the LandCover.AI 100 dataset (a small version of the LandCover.AI dataset for notebook testing), instantiate a `CustomSemanticSegmentationTask` with a U-Net and ResNet-50 backbone, then train the model using a Lightning trainer.\n", |
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| "The remainder of the turial is straightforward and follows the typical [PyTorch Lightning](https://lightning.ai/) training routine. We instantiate a `DataModule` for the LandCover.AI 100 dataset (a small version of the LandCover.AI dataset for notebook testing), instantiate a `CustomSemanticSegmentationTask` with a U-Net and ResNet-50 backbone, then train the model using a Lightning trainer.\n", | |
| "The remainder of the turial is straightforward and follows the typical [PyTorch Lightning](https://lightning.ai/) training routine. We instantiate a `DataModule` for the LandCover.AI 100 dataset (a small version of the LandCover.AI dataset for notebook testing), instantiate a `CustomSemanticSegmentationTask` with a U-Net and ResNet-18 backbone, then train the model using a Lightning trainer.\n", |
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Re-upped version of #1897
For review convenience, here's a link to the notebook.