Log images

Logging images in different format

Log images under the current run at the given step.

Use the function log_images for a run.
PIL package is needed to log images. To install the PIL package, run

pip install pillow

Here is the sample code to log images from different sources:

import mlfoundry
import numpy as np
import PIL.Image

client = mlfoundry.get_client()
run = client.create_run(

imarray = np.random.randint(low=0, high=256, size=(100, 100, 3))
im = PIL.Image.fromarray(imarray.astype("uint8")).convert("RGB")

images_to_log = {
    "logged-image-array": mlfoundry.Image(data_or_path=imarray),
    "logged-pil-image": mlfoundry.Image(data_or_path=im),
    "logged-image-from-path": mlfoundry.Image(data_or_path="result_image.jpeg"),

run.log_images(images_to_log, step=1)

Visualizing the logged images

You can visualize the logged images in the MLFoundry dashboard.


Logged Images

You can also view the images logged step-wise by clicking in an image.


Visualizing image at different steps

Class mlfoundry.Image

Images are represented and logged using this class in mlfoundry.

You can initialize mlfoundry.Image by either by using a local path or you can use a numpy array / PIL.Image object.

You can also log caption and the actual and prodicted values for an image as shown in the examples below.

Logging images with caption and a class label

from keras.datasets import mnist
import mlfoundry as mlf
import time
import numpy as np

data = mnist.load_data()
(X_train, y_train), (X_test, y_test) = data

client = mlf.get_client()
run = client.create_run("mnist-sample")

actuals = list(y_test)
predictions = list(np.random.randint(9, size=10))

img_dict = {}
for i in range(10):
    img_dict[str(i)] = mlf.Image(
        caption="mnist sample",
            "actuals": str(actuals[i]), 
            "predictions": str(predictions[i])


The logged images can be visualized in the mlfoundry dashboard.


MNIST sample images

Logging image with multi-label classification problems

images_to_log = {
    "logged-image-array": mlfoundry.Image(
        caption="testing image logging",
        class_groups={"actuals": ["dog", "human"], "predictions": ["cat", "human"]},

run.log_images(images_to_log, step=1)