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Last updated Feb 22, 2024

Image Classification

Out of the Box Packages > UiPath Image Analysis > Image Classification

Note:

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.

Model Details

Input Type

FILE

Input Description

Full path of the image file on which you want to classify.

Make sure that the image format is either JPEG or PNG.

Output Description

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
  }
}

Recommend GPU

By default, a GPU is recommended.

Training Enabled

By default, training is enabled.

Pipelines

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).

Dataset Format

For training and evaluation datasets, point to a folder with a subfolder called 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
      -- Car

Environment Variables

  • Epochs - default value 20

Artifacts

Classification 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        20

Confusion 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

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