Further Usage

Saving and Loading

After training a model you’re happy with, saving and loading it from a file is easy. To save a model to a file (the recommended file extension is .pth), use the save method:

from detecto.core import Model

labels = ['label1', 'label2', '...']
model = Model(labels)
# ... training and other steps ...

model.save('your_save_file.pth')

To load a model from a file, use the static load method:

model = Model.load('your_save_file.pth', labels)

Be sure that the list of labels you provide is in the same order as when you first initialized and saved the model.

Beyond Detecto

Detecto abstracts away a lot of the details of machine learning, and at a certain point, you may decide you want more control. Since Detecto is built on top of PyTorch and torchvision, transitioning to these feature-rich libraries is easy. Simply use the get_internal_model method to access the underlying torchvision model that Model uses:

torch_model = model.get_internal_model()
print(type(torch_model))

The internal model is a Faster R-CNN ResNet-50 FPN with a FastRCNNPredictor box predictor. With the torchvision model itself, you can now fine-tune the model accuracy, modify the model architecture, and do many more things using the various PyTorch and torchvision modules.

For example, the following code limits fine-tuning during training to only the last few layers of the model:

for name, p in torch_model.named_parameters():
    print(name, p.requires_grad)

    if 'roi_heads' not in name and 'rpn' not in name:
        p.requires_grad = False

# Can then proceed to train your Detecto model as usual
model.fit(...)