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