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Image Classification
Out of the Box Packages > UiPath Image Analysis > Image Classification
The Image Classification model is currently in public preview.
UiPath® is committed to stability and quality of our products, but preview features are always subject to change based on feedback that we receive from our customers. Using preview features is not recommended for production deployments.
This model runs smoothly on CPU, but you may encounter issues while running on GPU during the preview.
This preview model is a retrainable deep learning model used to classify images. You can train it on your own data and create an ML Skill to perform image classification. This ML Package must be retrained, if deployed without training first, deployment will fail with an error stating that the model is not trained.
Full path of the image file on which you want to classify.
Make sure that the image format is either JPEG or PNG.
JSON with identified label for the image and confidence score (between 0-1).
{
"response": {
"label": "car",
"confidence": 0.85657345056533813
}
}{
"response": {
"label": "car",
"confidence": 0.85657345056533813
}
}All three types of pipelines (Full Training, Training, and Evaluation) are supported by this package. For most use cases, no parameters need to be specified, the model is using advanced techniques to find a performant model. In subsequent trainings after the first, the model uses incremental learning (that is, the previously trained version will be used, at the end of a Training Run).
images and this subfolder can contain input several folders with different classes (for example, a folder called cats with pictures of cats, and another one called dogs with pictures of dogs, and so on).
Example :
-- <Training / Evaluation Directory>
-- images
-- Bus
-- bus001.jpg
-- bus002.jpg
-- bus003.jpg
-- Truck
-- truck001.jpg
-- truck012.png
-- truck0030.jpeg
-- Car-- <Training / Evaluation Directory>
-- images
-- Bus
-- bus001.jpg
-- bus002.jpg
-- bus003.jpg
-- Truck
-- truck001.jpg
-- truck012.png
-- truck0030.jpeg
-- CarClassification report
precision recall f1-score support
Positive 0.75 0.90 0.82 10
Negative 0.88 0.70 0.78 10
accuracy 0.80 20
macro avg 0.81 0.80 0.80 20
weighted avg 0.81 0.80 0.80 20precision recall f1-score support
Positive 0.75 0.90 0.82 10
Negative 0.88 0.70 0.78 10
accuracy 0.80 20
macro avg 0.81 0.80 0.80 20
weighted avg 0.81 0.80 0.80 20Confusion matrix
Predictions.csv
This is a CSV file with predictions on the test set used for evaluation.
filename actual predicted
38 00043.jpg Positive Positive
17 00001.jpg Positive Positive
59 00014.jpg Negative Positive
31 00015.jpg Positive Positive
15 00008.jpg Positive Positive
69 00025.jpg Negative Negative
49 00003.jpg Positive Positive
5 00034.jpg Positive Positive
36 00044.jpg Positive Positive
50 00042.jpg Negative Positive
96 00011.jpg Negative Negative
53 00046.jpg Negative Positive
94 00036.jpg Negative Negativefilename actual predicted
38 00043.jpg Positive Positive
17 00001.jpg Positive Positive
59 00014.jpg Negative Positive
31 00015.jpg Positive Positive
15 00008.jpg Positive Positive
69 00025.jpg Negative Negative
49 00003.jpg Positive Positive
5 00034.jpg Positive Positive
36 00044.jpg Positive Positive
50 00042.jpg Negative Positive
96 00011.jpg Negative Negative
53 00046.jpg Negative Positive
94 00036.jpg Negative Negative
