Fine-tuning a Spotiflow model on a custom dataset¶

Data format¶

See Data format.

Fine-tuning (CLI)¶

You can fine-tune from an existing model by simply adding an argument to the CLI call. See Basic training (CLI) for more information.

spotiflow-train /path/to/spots_data -o /my/trained/model --finetune-from general

where /path/to/pretrained/model is the path to the directory containing the model you want to fine-tune. You can also pass other parameters to the training, such as the number of epochs, the learning rate, etc. For more information on the arguments allowed, see the documentation of the CLI command:

Fine-tuning (API)¶

Finetuning a pre-trained model on a custom dataset is very easy. You can load the model very similarly to what you would normally do to predict on new images (you only need to add one extra parameter!):

from spotiflow.model import Spotiflow
from spotiflow.utils import get_data

# Get the data
train_imgs, train_spots, val_imgs, val_spots = get_data("/path/to/spots_data")

# Initialize the model
model = Spotiflow.from_pretrained(
    "general",
    inference_mode=False,
)

# Train and save the model
model.fit(
    train_imgs,
    train_spots,
    val_imgs,
    val_spots,
    save_dir="/my/trained/model",
)

Of course, you can also fine-tune from a model you have trained before. In that case, use the from_folder() method instead of from_pretrained() (see Predicting spots in an image). All the information about training customization from Customizing the training applies here as well. However, note that you cannot change the model architecture when fine-tuning!