Resisc45 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 Resisc45 dataset.

from aitlas.datasets import Resisc45Dataset
from aitlas.models import ResNet50
from aitlas.transforms import ResizeCenterCropFlipHVToTensor, ResizeCenterCropToTensor
from aitlas.utils import image_loader

Load the dataset

dataset_config = {
    "data_dir": "./data/RESISC45",
    "csv_file": "./data/RESISC45/train.csv"
}
dataset = Resisc45Dataset(dataset_config)

Show images from the dataset

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

Inspect the data

dataset.show_samples()

dataset.data_distribution_table()

fig = dataset.data_distribution_barchart()

Load train and test splits

train_dataset_config = {
    "batch_size": 16,
    "shuffle": True,
    "num_workers": 4,
    "data_dir": "./data/RESISC45",
    "csv_file": "./data/RESISC45/train.csv"
}

train_dataset = Resisc45Dataset(train_dataset_config)
train_dataset.transform = ResizeCenterCropFlipHVToTensor()

test_dataset_config = {
    "batch_size": 4,
    "shuffle": False,
    "num_workers": 4,
    "data_dir": "./data/RESISC45",
    "csv_file": "./data/RESISC45/test.csv",
    "transforms": ["aitlas.transforms.ResizeCenterCropToTensor"]
}

test_dataset = Resisc45Dataset(test_dataset_config)
len(train_dataset), len(test_dataset)

Setup and create the model for training

epochs = 10
model_directory = "./experiments/RESISC45"
model_config = {
    "num_classes": 45,
    "learning_rate": 0.0001,
    "pretrained": True,
    "metrics": ["accuracy", "precision", "recall", "f1_score"]
}
model = ResNet50(model_config)
model.prepare()

Training and evaluation

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

Predictions

model_path = "./experiments/RESISC45/checkpoint.pth.tar"
#labels = Resisc45Dataset.labels
labels = ["airplane", "airport", "baseball_diamond", "basketball_court", "beach", "bridge", "chaparral", "church",
          "circular_farmland", "cloud", "commercial_area", "dense_residential", "desert", "forest", "freeway",
          "golf_course", "ground_track_field", "harbor", "industrial_area", "intersection", "island", "lake",
          "meadow", "medium_residential", "mobile_home_park", "mountain", "overpass", "palace", "parking_lot",
          "railway", "railway_station", "rectangular_farmland", "river", "roundabout", "runway", "sea_ice",
          "ship", "snowberg", "sparse_residential", "stadium", "storage_tank", "tennis_court", "terrace",
          "thermal_power_station", "wetland"]
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)