Model Settings and Reports
  • 28 May 2024
  • 3 Minutes to read
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Model Settings and Reports

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Article summary

After you train a model, LandingLens creates a report for that model. To view a model's report:

  1. Open the project.
  2. Click Models at the top of the page.
    Open Models
  3. Select the model from the Model drop-down menu.
    Select the Model
  4. Click the Actions icon (...) next to the model name.
    Click the Actions Icon
  5. View the displayed report, which has these tabs:

Rename Models

You can rename models at any time. Model names can include letters, numbers, and special characters. To rename models:

  1. Open the project.
  2. Click Models at the top of the page.
    Open Models
  3. Select the model you want to rename from the Model drop-down menu.
    Select the Model
  4. Click the Actions icon (...) next to the model name.
    Click the Actions Icon
  5. Click the Edit (pencil) icon next to the model name and enter a new name.
    Rename the Model

Settings

The Settings tab shows the model Learning Curve and Configuration

The Learning Curve graph shows how the model performed during training (y axis), and how long it took to train the model (x axis).

Model Training Learning Curve
Note:
When you train a model, a virtual computer in the cloud warms up. Because of this step, the start time on the graph is not 0 seconds. Instead, the graph starts after the amount of time it took to acquire the virtual computer.

The Configuration section provides more details on your model training. For example, you can see how many epoch cycles your model went through.

Model Training Configuration

Data Snapshot

The Data Snapshot tab shows all the images, labels, and predictions included in the dataset for the model. On this page, you can also download a CSV of the snapshot data.

To see a snapshot for the data in the model without the model's predictions, go to the Snapshot dashboard

Data Snapshot

Download a CSV of Dataset Data

For Object Detection and Classification projects, click Download CSV to download a CSV of information about the dataset. The CSV includes several columns of data, including Project Name, Image Name, Split.

To download a CSV of dataset data:

  1. Open the project.
  2. Click Models at the top of the page.
    Open Models
  3. Select the model that has the dataset you're interested in from the Modeldrop-down menu.
    Select the Model
  4. Click the Actions icon (...) next to the model name.
    Click the Actions Icon
  5. Click the Dataset Snapshottab.
    Data Snapshot
  6. Click the Actions icon (...) and select Download CSV.
    Download the CSV
  7. The file is downloaded to your computer. For a description of all data in the file, go to CSV Data.

CSV Data

When you download a CSV of a dataset, the file includes the information described in the following table.

ItemDescriptionExample
Project NameName of the LandingLens project.Defect Detection
Project TypeProject type ("bounding_box" is Object Detection).classification
Image NameThe file name of the image uploaded to LandingLens.sample_003.jpg
Image IDUnique ID assigned to the image.29786892
SplitThe split assigned to the image.train
Upload TimeThe time the image was uploaded to LandingLens. All times are in Coordinated Universal Time (UTC).Mon Jun 26 2023 16:37:10 GMT+0000 (Coordinated Universal Time)
Image WidthThe width (in pixels) of the image when it was uploaded to LandingLens.4771
Image HeightThe height (in pixels) of the image when it was uploaded to LandingLens.2684
Model NameThe name of the model in LandingLens.100% Precision and Recall
MetadataAny metadata assigned to the image. If the image doesn't have any metadata, the value is "{}". {"Author":"Eric Smith","Organization":"QA"}
GT_ClassThe Classes you assigned to the image (ground truth or “GT”) .

For Object Detection, this also includes the number of objects you labeled. 
{"Screw":3}
PRED_ClassThe Classes the model predicted.

For Object Detection, this also includes the number of objects predicted.

If the model didn't predict any objects, the value is {"null":1}.
{"Screw":2}
Model_CorrectIf the model's prediction matched the original label (ground truth or “GT”), the value is true.

If the model's prediction didn't match the original label (ground truth or “GT”), the value is false.

Only applicable to Classification projects.
true
PRED_Class_Confidence / PRED_ConfidenceThe model's Confidence Score for each object predicted.

If the model didn't predict any objects, the value is {}.
[{"Screw":0.94796216},{"Screw":0.9787127}]
Class_TotalAreaThe total area (in pixels) of the model's predicted area.

If the model didn't predict any objects, the value is {}.

Only applicable to Object Detection projects.
{"Screw":76060}
GT-PRED JSONThe JSON output comparing the original labels (ground truth or "GT") to the model's predictions. For more information, go to JSON Output.{"gtDefectName":"No Fire","predDefectName":"No Fire","predConfidence":0.9684047}

Logs

The Logs tab shows data that can be helpful for troubleshooting. If you experienced any issues during model training, you can provide these logs to a LandingAI representative.

Model Logs

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