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BaaLΒΆ

The framework Bayesian Active Learning (BaaL) is an active learning library developed at ElementAI.

Active Learning is a sub-field in AI, focusing on adding a human in the learning loop. The most uncertain samples will be labelled by the human to accelerate the model training cycle.

Credit to ElementAI / Baal Team for creating this diagram flow


With its integration within Flash, the Active Learning process is simpler than ever before.

import torch

import flash
from flash.core.classification import ProbabilitiesOutput
from flash.core.data.utils import download_data
from flash.image import ImageClassificationData, ImageClassifier
from flash.image.classification.integrations.baal import ActiveLearningDataModule, ActiveLearningLoop

# 1. Create the DataModule
download_data("https://pl-flash-data.s3.amazonaws.com/hymenoptera_data.zip", "./data")

# Implement the research use-case where we mask labels from labelled dataset.
datamodule = ActiveLearningDataModule(
    ImageClassificationData.from_folders(train_folder="data/hymenoptera_data/train/", batch_size=2),
    initial_num_labels=5,
    val_split=0.1,
)

# 2. Build the task
head = torch.nn.Sequential(
    torch.nn.Dropout(p=0.1),
    torch.nn.Linear(512, datamodule.num_classes),
)
model = ImageClassifier(
    backbone="resnet18", head=head, num_classes=datamodule.num_classes, output=ProbabilitiesOutput()
)


# 3.1 Create the trainer
trainer = flash.Trainer(max_epochs=3)

# 3.2 Create the active learning loop and connect it to the trainer
active_learning_loop = ActiveLearningLoop(label_epoch_frequency=1)
active_learning_loop.connect(trainer.fit_loop)
trainer.fit_loop = active_learning_loop

# 3.3 Finetune
trainer.finetune(model, datamodule=datamodule, strategy="freeze")

# 4. Predict what's on a few images! ants or bees?
predictions = model.predict("data/hymenoptera_data/val/bees/65038344_52a45d090d.jpg")
print(predictions)

# 5. Save the model!
trainer.save_checkpoint("image_classification_model.pt")
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