manot

The manot SDK is a wrapper on top of our API to make it easier to work with our model performance monitoring system. Using our SDK you can quickly set up your project by defining a few key parameters, including the paths to your data, classes and model. Once the project is set up you will be able to use the insight method to extract outliers that manot has detected on the new unstructured data that the performance of the model is evaluated on.

Installation

Install manot with pip:

pip install manot

Example

This is an example how to start:

from manot import manotAI

manot = manotAI("manot_service_url", "token")

# Setup process for "local" and "s3" providers
setup = manot.setup(
    data_provider="local", # it must be "s3" or "local"
    arguments={
            "name": "setup_example",
            "images_path": "/path/to/images",
            "ground_truths_path": "/path/to/ground_truths",
            "detections_path": "/path/to/detections",
            "detections_metadata_format": "xyx2y2",  # it must be one of "xyx2y2", "xywh", or "cxcywh"
            "classes_txt_path": "/path/to/classes.txt"
        }
)

# Setup process for deeplake provider
setup = manot.setup(
    data_provider="deeplake",
    arguments={
            "name": "setup_example",
            "detections_metadata_format": "xyx2y2",  # it must be one of "xyx2y2", "xywh", or "cxcywh"
            "deeplake_token": "your deeplake token",
            "data_set": "user/dataset/",
            "detections_boxes_key": "deeplake key where detection boxes are stored",
            "detections_labels_key": "deeplake key where where detection labels are stored",
            "detections_score_key": "deeplake key where detections score is stored",
            "ground_truths_boxes_key": "deeplake key where ground truth boxes are stored",
            "ground_truths_labels_key": "deeplake key where ground truth labels are stored",
            "classes": "classes for deeplake"
        }
)
print(setup)
# {"id": setup_id, "name": "setup_example", "status": "started"}

setup_info = manot.get_setup(setup["id"])
# when setup is successfully finished, then setup_info is {"id": setup_id, "name": "setup_example", "status": "started"}

insight = manot.insight(
    name="insight_example",
    setup_id=setup["id"],
    data_path="/path/to/data",
    data_provider="local",  # it must be "s3" or "local"
    percentage="percentage" # percentage of images to be considered insight should be larger than 0 and less or equal than 100
)
print(insight)
# {"id": insight_id, "name": "insight_example", "status": "started"}

insight_info = manot.get_insight(insight["id"])
# when setup is successfully finished, then insight_info is {"id": insight_id, "name": "setup_example", "status": "started"}

scores = manot.get_scores(insight['id'])
#returns list of all processed images graded by their score from 0 to 10 (higher is more impactful image)

#in case of deeplake please also provide deeplake token 
manot.visualize_data_set(insight_info['data_set']['id'], deeplake_token)

# Upload data for Setup or Insights process
manot.upload_data(dir_path="/path/to/data", process="process_name")

For Setup process

• dir_path is directory path, which must contain images, detections, and ground_truths folders and classes.txt file.
• process must be "setup".

For Insight process

• dir_path is directory path, which must contain data. Data formats must be ".jpeg", ".jpg", ".png", ".avi", ".gif", ".m4v", ".mkv" or ".mp4".
• process must be "insight".

manot.calculate_map(
    ground_truths_path="/path/to/ground_truths",
    detections_path="/path/to/detections",
    classes_txt_path="/path/to/classes.txt",
    data_provider="local",  # it must be "s3" or "local"
    data_set_id="data_set_id",  # if data_set_id is provided will calculate mAP only on selected data, otherwise will calculate mAP on all the data
)

Resources

• API Documentation
• Jupyter Example