RSI CB256 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 RSI CB256 dataset.

from aitlas.datasets import RSICB256Dataset
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/RSI-CB256",
    "csv_file": "./data/RSI-CB256/train.csv"
}
dataset = RSICB256Dataset(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/RSI-CB256",
    "csv_file": "./data/RSI-CB256/train.csv"
}

train_dataset = RSICB256Dataset(train_dataset_config)
train_dataset.transform = ResizeCenterCropFlipHVToTensor()

test_dataset_config = {
    "batch_size": 4,
    "shuffle": False,
    "num_workers": 4,
    "data_dir": "./data/RSI-CB256",
    "csv_file": "./data/RSI-CB256/test.csv",
    "transforms": ["aitlas.transforms.ResizeCenterCropToTensor"]
}

test_dataset = RSICB256Dataset(test_dataset_config)
len(train_dataset), len(test_dataset)

Setup and create the model for training

epochs = 10
model_directory = "./experiments/RSI-CB256"
model_config = {
    "num_classes": 35,
    "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/RSI-CB256/checkpoint.pth.tar"
#labels = RSICB256Dataset.labels
labels = ["airplane", "airport_runway", "artificial_grassland", "avenue", "bare_land", "bridge", "city_building",
          "coastline", "container", "crossroads", "dam", "desert", "dry_farm", "forest", "green_farmland", "highway",
          "hirst", "lakeshore", "mangrove", "marina", "mountain", "parkinglot", "pipeline", "residents", "river",
          "river_protection_forest", "sandbeach", "sapling", "sea", "shrubwood", "snow_mountain", "sparse_forest",
          "storage_room", "stream", "town"]
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)