PatterNet dataset: Example of the aitlas toolbox in for multi class image classification#

This notebook shows a sample implementation of a multi class image classification using the aitlas toolbox and the PatternNet dataset.

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from aitlas.datasets import PatternNetDataset
from aitlas.models import ResNet50
from aitlas.transforms import ResizeCenterCropFlipHVToTensor, ResizeCenterCropToTensor
from aitlas.utils import image_loader

Load the dataset#

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dataset_config = {
    "data_dir": "./data/PatternNet",
    "csv_file": "./data/PatternNet/train.csv"
}
dataset = PatternNetDataset(dataset_config)

Show images from the dataset#

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fig1 = dataset.show_image(1000)
fig2 = dataset.show_image(80)
fig3 = dataset.show_batch(15)

Inspect the data#

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dataset.show_samples()
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dataset.data_distribution_table()
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fig = dataset.data_distribution_barchart()

Load train and test splits#

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train_dataset_config = {
    "batch_size": 16,
    "shuffle": True,
    "num_workers": 4,
    "data_dir": "./data/PatternNet",
    "csv_file": "./data/PatternNet/train.csv"
}

train_dataset = PatternNetDataset(train_dataset_config)
train_dataset.transform = ResizeCenterCropFlipHVToTensor()

test_dataset_config = {
    "batch_size": 4,
    "shuffle": False,
    "num_workers": 4,
    "data_dir": "./data/PatternNet",
    "csv_file": "./data/PatternNet/test.csv",
    "transforms": ["aitlas.transforms.ResizeCenterCropToTensor"]
}

test_dataset = PatternNetDataset(test_dataset_config)
len(train_dataset), len(test_dataset)

Setup and create the model for training#

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epochs = 10
model_directory = "./experiments/PatternNet"
model_config = {
    "num_classes": 38,
    "learning_rate": 0.0001,
    "pretrained": True,
    "metrics": ["accuracy", "precision", "recall", "f1_score"]
}
model = ResNet50(model_config)
model.prepare()

Training and evaluation#

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model.train_and_evaluate_model(
    train_dataset=train_dataset,
    epochs=epochs,
    model_directory=model_directory,
    val_dataset=test_dataset,
    run_id='1',
)

Predictions#

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model_path = "./experiments/PatternNet/checkpoint.pth.tar"
#labels = PatternNetDataset.labels
labels = ["airplane", "baseball_field", "basketball_court", "beach", "bridge", "cemetery", "chaparral",
          "christmas_tree_farm", "closed_road", "coastal_mansion", "crosswalk", "dense_residential",
          "ferry_terminal", "football_field", "forest", "freeway", "golf_course", "harbor", "intersection",
          "mobile_home_park", "nursing_home", "oil_gas_field", "oil_well", "overpass", "parking_lot", "parking_space",
          "railway", "river", "runway", "runway_marking", "shipping_yard", "solar_panel", "sparse_residential",
          "storage_tank", "swimming_pool", "tennis_court", "transformer_station", "wastewater_treatment_plant"]
transform = ResizeCenterCropToTensor()
model.load_model(model_path)

image = image_loader('./data/predict/image1.tif')
fig = model.predict_image(image, labels, transform)

image = image_loader('./data/predict/image2.tif')
fig = model.predict_image(image, labels, transform)

image = image_loader('./data/predict/image3.tif')
fig = model.predict_image(image, labels, transform)

image = image_loader('./data/predict/image4.tif')
fig = model.predict_image(image, labels, transform)