Training/Inference with Image Modality

If your training and inference tasks involve image modality, your data would contain a column representing the path to the image file, i.e.

   feature_1                     image   label
0          1   image/train/train_1.png       0
1          2   image/train/train_1.png       1

Preparing the Image Column

Currently, AutoGluon Cloud only supports one image per row. If your dataset contains one or more images per row, we first need to preprocess the image column to only contain the first image of each row.

For example, if your images are seperated with ;, you can preprocess it via:

# image_col is the column name containing the image path. In the example above, it would be `image`
train_data[image_col] = train_data[image_col].apply(lambda ele: ele.split(';')[0])
test_data[image_col] = test_data[image_col].apply(lambda ele: ele.split(';')[0])

Now we update the path to an absolute path.

For example, if your directory is similar to this:

.
└── current_working_directory/
    ├── train.csv
    ├── test.csv
    └── images/
        ├── train/
        │   └── train_1.png
        └── test/
            └── test_1.png

You can replace your image column to absolute paths via:

train_data[image_col] = train_data[image_col].apply(lambda path: os.path.abspath(path))
test_data[image_col] = test_data[image_col].apply(lambda path: os.path.abspath(path))

Perform Training/Inference with Image Modality

Provide argument image_column as the column name containing image paths to CloudPredictor fit/inference APIs along with other arguments that you would normally pass to a CloudPredictor In the example above, image_column would be image

cloud_predictor = TabularCloudPredictor(cloud_output_path="YOUR_S3_BUCKET_PATH")
cloud_predictor.fit(..., image_column="IMAGE_COLUMN_NAME")
cloud_predictor.predict_real_time(..., image_column="IMAGE_COLUMN_NAME")
cloud_predictor.predict(..., image_column="IMAGE_COLUMN_NAME")