Testing Multilabel image classification model

This code example shows how to use this library to test a multilabel image classification model from scratch, using an already trained model. You can also create a movie from the classified images with predicted labels on them.

Evaluating the multilabel classification model

You can use this library to evaluate the trained multilabel image classification model with different tools. The simplest test to do is evaluating the f1-score on different datasets where you could just place the images in say a val/ directory. The data is in a directory with the following structure:


First, the list of classes or categories should be extracted using the following code:

categories = decavision.utils.utils.load_classes(gs_folder)

Then the evaluation of the model is done using the following code:

tester = decavision.model_testing.testing.ModelTesterMultilabel(model=model_name, categories=categories)
tester.evaluate(path="image_dataset/val", json_file="image_dataset/classes.json")

Plot & Save classified images

You can also explicitly look at the classified images with predictions on the fly. To do so use the following function:

tester.classify_images(path="image_dataset/val", json_file="image_dataset/classes.json", plot=True, save_img=True)

In order to save images, you will need to specify plot=True and save_img=True. You will not be able to save images without plotting, this will be updated in the next version of the library.


Set plot=True, save_img=True to save classified images.

Create a movie from classified images

There are two ways to create a movie from classified images: you can directly run the following code by setting classify_images=True to make predictions on new images -> save classified images to a folder and create a movie from them:

tester.create_movie(path="image_dataset/val", json_file="image_dataset/classes.json", classify_images=True)

If you already have classified saved images in a folder, you can set classify_images=False and pass an optional argument which will be the path to the classified saved images directory image_folder=classified_image_path. Assume the classified images are saved under image_dataset/classified_images/, then:

tester.create_movie(path=path, json_file="image_dataset/classes.json", classify_images=False, image_folder="image_dataset/classified_images")

Generate Classification report and Confusion matrix

Finally, you can also generate a confusion matrix and classification report using the function:

tester.generate_metrics(path="image_dataset/val", json_file)