Catalyst

Load image data using `catalyst.contrib.datasets.CIFAR10` or `MNIST`.

Define a convolutional neural network (CNN) model.

Use `SupervisedRunner` to train the model with cross-entropy loss and an Adam optimizer.

Apply `AccuracyCallback` to track classification accuracy during training.

Save the trained model weights using `CheckpointCallback`.

Load image and mask data.

Define a U-Net model.

Use `SupervisedRunner` to train the model with Dice loss.

Apply `DiceCallback` to track segmentation accuracy during training.

Visualize segmentation results using `catalyst.utils.visualization`.

Load text data and preprocess it using tokenization and padding.

Define a recurrent neural network (RNN) or transformer model.

Use `SupervisedRunner` to train the model with cross-entropy loss and an Adam optimizer.

Apply `AccuracyCallback` to track classification accuracy during training.

Evaluate the model on a held-out test set.

Define a generator and discriminator model.

Use a custom `Runner` to implement the GAN training loop.

Define separate optimizers for the generator and discriminator.

Track generator and discriminator loss using custom callbacks.

Generate sample images during training to monitor progress.

Trace the model using `catalyst.utils.trace_model`.

Quantize the model using `catalyst.utils.quantize_model`.

Prune the model using `catalyst.utils.prune_model`.

Export the optimized model to ONNX format using `catalyst.utils.onnx_export`.

Deploy the optimized model on a target device.