Using supplied training data

cellfinder is released with a pre-trained cell candidate classification network, trained on approximately 100,000 manually annotated cell candidates (with a roughly 50/50 split between cells and non-cells).
This data was acquired using serial two-photon tomography. While you will likely need to retrain the network for your own data, we make the data available for a few reasons:
You might want to use this data to test the training, or assess how much training data you may need
You might want to retrain a different network (i.e. a different ResNet depth) than the one supplied (50-layer).
You might want to retrain the network using a mixture of this data (of which there is a lot) and your data (of which you may not be able to generate as much).
The data is available here. To retrain the network using just this data, download the data, extract the tar archive, and then follow these steps:
If you're using Windows, you will need to edit training.yml so that the paths (in each cube_dir and cell_def entry) match windows paths (i.e. backslashes)
Activate your conda environment:
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conda activate cellfinder
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Navigate to the training data directory
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cd serial2p
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Start training
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cellfinder_train -y training.yml -o training_output
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The training will likely take a few minutes to get going, once the network starts you should see something like this:
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Epoch 1/100
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   1/6050 [..............................] - ETA: 0s - loss: 0.9579 - accuracy:    
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   2/6050 [..............................] - ETA: 1:33:47 - loss: 3.1335 - accur   
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   3/6050 [..............................] - ETA: 3:10:17 - loss: 2.6173 - accur   
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   4/6050 [..............................] - ETA: 4:03:42 - loss: 2.2663 - accur   
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   5/6050 [..............................] - ETA: 4:30:16 - loss: 2.0002 - accur
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Training the network

Visualisation

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