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Model Training
You can train a PyTorch model without any coding.
Training is as simple as a single click!
Currently, classification models are supported for following architecture (node names are in parentheses):
- Linear (Ptn Linear Model)
- Convolutional Network (Ptn Conv Model)
- ResNet (Ptn Resnet Model)
You can adjust the model configuration including output dimension on the UI.
The system is designed to be modular. For example, to switch from a linear model to a convolutional model, you only need to replace the model node (see screenshot below).
You can choose to:
- Use your own images
- Use a public dataset
Using Your Own Images
Check out the dog and cat classification tutorial.
Public Datasets
Currently, only Fashion MNIST has been tested. Other public datasets have not been verified yet, though they may work.
If you try one and it doesn't work, please create an issue so I can investigate adding support.
To use the Fashion MNIST dataset, simply drag and drop the workflow files below into ComfyUI. The model will be downloaded automatically.
Linear model
- Training: examples/fashion_mnist_train.json
- Evaluation: examples/fashion_mnist_eval.json
Convolutional model
- Training: examples/fashion_mnist_train_conv.json
- Evaluation: examples/fashion_mnist_eval_conv.json
These training workflows train a FashionMNIST model from scratch effortlessly.
Note: Before you try these workflows, please install ComfyUI-Data-Analysis extension.
Training workflow
Linear model
Conv model
Close up of the conv node
Eval workflow
Linear model
Conv model
