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| <qt> | |
| Check out this <a href="https://youtu.be/3Y1VKcxjNy4">video</a> to learn the process. | |
| <ol> | |
| <li>Drag and drop an image from a folder of images with a similar style (like similar cell types).</li> | |
| <li>Run the built-in models on one of the images using the "model zoo" and find the one that works best for your | |
| data. Make sure that if you have a nuclear channel you have selected it for CHAN2. | |
| </li> | |
| <li>Fix the labelling by drawing new ROIs (right-click) and deleting incorrect ones (CTRL+click). The GUI | |
| autosaves any manual changes (but does not autosave after running the model, for that click CTRL+S). The | |
| segmentation is saved in a "_seg.npy" file. | |
| </li> | |
| <li> Go to the "Models" menu in the File bar at the top and click "Train new model..." or use shortcut CTRL+T. | |
| </li> | |
| <li> Choose the pretrained model to start the training from (the model you used in #2), and type in the model | |
| name that you want to use. The other parameters should work well in general for most data types. Then click | |
| OK. | |
| </li> | |
| <li> The model will train (much faster if you have a GPU) and then auto-run on the next image in the folder. | |
| Next you can repeat #3-#5 as many times as is necessary. | |
| </li> | |
| <li> The trained model is available to use in the future in the GUI in the "custom model" section and is saved | |
| in your image folder. | |
| </li> | |
| </ol> | |
| </qt> |