AI4EO Rapid Prototyping Enviro AiTLAS

📆 Project Period	October, 2023
📍 GitHub	gitlab.esa.int

Example of the `aitlas` toolbox in the context of benchmarking a dataset

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

import pandas as pd
import matplotlib.pyplot as plt

from aitlas.datasets import UcMercedDataset
from aitlas.models import VGG16
from aitlas.tasks import StratifiedSplitTask

Define the splits and dataset paths

splits = [(10, 90), (20, 80), (30, 70), (40, 60), (50, 50), (60, 40), (70, 30),
          (80, 20), (90, 10)] # one tuple is (train, test)
data_dir = "./media/hdd/multi-class/UCMerced" # where is the dataset on disk

Loop through the splits, train and evaluate

results = [] # results accumulator

# iterate through the splits
for train, test in splits:
    # where to store the file names of the train images
    train_csv_file = f"/home/jovyan/work/AiTLAS/media/hdd/multi-class/UCMerced/train_{train}_{test}.csv"
    #train_csv_file = f"/UCMerced/multitrain_{train}_{test}.csv"
    print(train_csv_file)
    # where to store the file names of the test images
    test_csv_file = f"/home/jovyan/work/AiTLAS/media/hdd/multi-class/UCMerced/test_{train}_{test}.csv"
    #test_csv_file = f"/UCMerced/test_{train}_{test}.csv"
    # configure split task
    split_config = {
        "split": {
            "train": {
                "ratio": train,
                "file": train_csv_file
            },
            "test": {
                "ratio": test,
                "file": test_csv_file
            }
        },
        "data_dir": data_dir
    }
    split_task = StratifiedSplitTask(None, split_config)
    split_task.run()

    # setup train set
    train_dataset_config = {
        "batch_size": 4,
        "shuffle": True,
        "num_workers": 4,
        "csv_file": train_csv_file,
        "data_dir": data_dir,
        "transforms": ["aitlas.transforms.ResizeCenterCropFlipHVToTensor"]
    }

    train_dataset = UcMercedDataset(train_dataset_config)

    # setup test set
    test_dataset_config = {
        "batch_size": 4,
        "shuffle": False,
        "num_workers": 4,
        "csv_file": test_csv_file,
        "data_dir": data_dir,
        "transforms": ["aitlas.transforms.ResizeCenterCropToTensor"]
    }

    test_dataset = UcMercedDataset(test_dataset_config)
    print(f"Train size: {len(train_dataset)}, Test size: {len(test_dataset)}")


    # setup model
    epochs = 1
    model_directory = "./experiments/uc_merced/"
    model_config = {
        "num_classes": 21,
        "learning_rate": 0.0001,
        "pretrained": True
    }
    model = VGG16(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',
    )

    # collect results
    results.append(model.running_metrics.f1_score())

See the results

df = pd.DataFrame(zip(splits, [round(float(r["F1_score Micro"]), 4) for r in results]),
                  columns=["Train/Test", "Micro F1 score"])
df

df.plot(x='Train/Test', y='Micro F1 score', kind = 'line')
plt.show()

AI4EO Rapid Prototyping Enviro AiTLAS

Example of the aitlas toolbox in the context of benchmarking a dataset

Define the splits and dataset paths

Loop through the splits, train and evaluate

See the results

Example of the `aitlas` toolbox in the context of benchmarking a dataset