UCI Merced dataset: Example of the aitlas toolbox in for multi class image classification

Contents

UCI Merced 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 using the UC merced dataset.

[1]:

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

Load the dataset#

[2]:

dataset_config = {
    "data_dir": "/media/hdd/multi-class/UCMerced",
    "csv_file": "/media/hdd/multi-class/UCMerced/trainval.csv"
}
dataset = UcMercedDataset(dataset_config)

Show images from the dataset#

[3]:

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

../_images/examples_multiclass_classification_example_uc_merced_5_0.png

../_images/examples_multiclass_classification_example_uc_merced_5_1.png

../_images/examples_multiclass_classification_example_uc_merced_5_2.png

Inspect the data#

[4]:

dataset.show_samples()

[4]:

	File name	Label
0	river/river10.tif	river
1	runway/runway09.tif	runway
2	tenniscourt/tenniscourt64.tif	tenniscourt
3	beach/beach17.tif	beach
4	runway/runway21.tif	runway
5	runway/runway11.tif	runway
6	harbor/harbor89.tif	harbor
7	freeway/freeway28.tif	freeway
8	parkinglot/parkinglot68.tif	parkinglot
9	freeway/freeway63.tif	freeway
10	runway/runway22.tif	runway
11	tenniscourt/tenniscourt81.tif	tenniscourt
12	parkinglot/parkinglot09.tif	parkinglot
13	chaparral/chaparral05.tif	chaparral
14	tenniscourt/tenniscourt15.tif	tenniscourt
15	forest/forest07.tif	forest
16	agricultural/agricultural29.tif	agricultural
17	agricultural/agricultural57.tif	agricultural
18	chaparral/chaparral28.tif	chaparral
19	agricultural/agricultural07.tif	agricultural

[5]:

dataset.data_distribution_table()

[5]:

	Label	Count
0	agricultural	100
1	airplane	100
2	baseballdiamond	100
3	beach	100
4	buildings	100
5	chaparral	100
6	denseresidential	100
7	forest	100
8	freeway	100
9	golfcourse	100
10	harbor	100
11	intersection	100
12	mediumresidential	100
13	mobilehomepark	100
14	overpass	100
15	parkinglot	100
16	river	100
17	runway	100
18	sparseresidential	100
19	storagetanks	100
20	tenniscourt	100

[6]:

fig = dataset.data_distribution_barchart()

../_images/examples_multiclass_classification_example_uc_merced_9_0.png

Load train and test splits#

[7]:

train_dataset_config = {
    "batch_size": 16,
    "shuffle": True,
    "num_workers": 4,
    "data_dir": "/media/hdd/multi-class/UCMerced",
    "csv_file": "/media/hdd/multi-class/UCMerced/train.csv"
}

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

test_dataset_config = {
    "batch_size": 4,
    "shuffle": False,
    "num_workers": 4,
    "data_dir": "/media/hdd/multi-class/UCMerced",
    "csv_file": "/media/hdd/multi-class/UCMerced/test.csv",
    "transforms": ["aitlas.transforms.ResizeCenterCropToTensor"]
}

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

[7]:

(1680, 420)

Setup and create the model for training#

[8]:

epochs = 10
model_directory = "/media/ssd/aitlas_new/examples/experiment/ucmerced"
model_config = {
    "num_classes": 21,
    "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#

[10]:

model_path = "/media/ssd/aitlas_new/examples/experiment/ucmerced/checkpoint.pth.tar"
#labels = UcMercedDataset.labels
labels = ["agricultural", "airplane", "baseballdiamond", "beach", "buildings", "chaparral",
                        "denseresidential", "forest", "freeway", "golfcourse", "harbor", "intersection",
                        "mediumresidential", "mobilehomepark", "overpass", "parkinglot", "river", "runway",
                        "sparseresidential", "storagetanks", "tenniscourt"]
transform = ResizeCenterCropToTensor()
model.load_model(model_path)

image = image_loader('/media/ssd/uc_merced_multilabel/predict/buildings98.tif')
fig = model.predict_image(image, labels, transform)

image = image_loader('/media/ssd/uc_merced_multilabel/predict/golfcourse04.tif')
fig = model.predict_image(image, labels, transform)

image = image_loader('/media/ssd/uc_merced_multilabel/predict/airplane18.tif')
fig = model.predict_image(image, labels, transform)

image = image_loader('/media/ssd/uc_merced_multilabel/predict/parkinglot13.tif')
fig = model.predict_image(image, labels, transform)

2021-11-25 01:23:11,459 INFO Loading checkpoint /media/ssd/aitlas_new/examples/experiment/ucmerced/checkpoint.pth.tar
2021-11-25 01:23:11,601 INFO Loaded checkpoint /media/ssd/aitlas_new/examples/experiment/ucmerced/checkpoint.pth.tar at epoch 11

../_images/examples_multiclass_classification_example_uc_merced_17_1.png

../_images/examples_multiclass_classification_example_uc_merced_17_2.png

../_images/examples_multiclass_classification_example_uc_merced_17_3.png

../_images/examples_multiclass_classification_example_uc_merced_17_4.png