- 01 Sep 2023
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Model Settings
- Updated on 01 Sep 2023
- 4 Minutes to read
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Model Reports
After you train a model, LandingLens creates a report for that model. To view a model's report:
- Open the project.
- Click Models at the top of the page.
Open Models
- Select the model from the Model drop-down menu.
Select the Model
- Click the Actions icon (...) next to the model name.
Click the Actions Icon
- View the displayed report, which has these tabs:
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).

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

Data Snapshot
The Data Snapshot tab shows all the images, labels, and predictions included in the dataset for the model. For more information about how to manage datasets, go to Dataset Versioning.

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 Landing AI associate.

Dataset Versioning
Each time you save a model, LandingLens also saves the whole data and the model's predictions. Saved data includes the images, labels, metadata, and labeling tasks. You can think of this as the "snapshot" of your dataset at that point in time.
You can:
- view each dataset in your project
- download a CSV of the data
- revert your project to an earlier dataset
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:
- Open the project.
- Click Models at the top of the page.
Open Models
- Select the model that has the dataset you're interested in from the Model drop-down menu.
Select the Model
- Click the Actions icon (...) next to the model name.
Click the Actions Icon
- Click the Dataset Snapshot tab.
Data Snapshot
- Click the Actions icon (...) and select Download CSV.
Download the CSV
- 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.
Item | Description | Example |
---|---|---|
Project Name | Name of the LandingLens project. | Defect Detection |
Project Type | Project type ("bounding_box" is Object Detection). | classification |
Image Name | The file name of the image uploaded to LandingLens. | sample_003.jpg |
Image ID | Unique ID assigned to the image. | 29786892 |
Split | The split assigned to the image. | train |
Upload Time | The 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 Width | The width (in pixels) of the image when it was uploaded to LandingLens. | 4771 |
Image Height | The height (in pixels) of the image when it was uploaded to LandingLens. | 2684 |
Model Name | The name of the model in LandingLens. | 100% Precision and Recall |
Metadata | Any metadata assigned to the image. If the image doesn't have any metadata, the value is "{}". | {"Author":"Eric Smith","Organization":"QA"} |
GT_Class | The 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_Class | The 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_Correct | If 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_Confidence | The 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_TotalArea | The 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 JSON | The 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} |
Revert to a Saved Dataset
If you are the Project Owner, you can revert your project to use one of the saved datasets. For example, let's say that you saved a model, and later added more images and labels. After training the model a second time, you see that the first model performed better, so you want to go back to that dataset. You can do this by reverting the dataset.
After you revert to a different dataset, you can change the dataset again, including to a dataset that was trained later.
Only Project Owners can revert the dataset. The button to revert datasets does not display to other users.
To revert to a saved dataset:
- Open the project.
- Click Models at the top of the page.
Open Models
- Select the model that has the dataset you want to revert to from the Model drop-down menu.
Select the Model
- Click the Actions icon (...) next to the model name.
Click the Actions Icon
- Click the Dataset Snapshot tab.
Data Snapshot
- Click the Actions icon (...) and select Revert current project to this data version.
Revert to the Selected Dataset
- Read the information on the pop-up and click Yes, Revert.
Confirm That You Want to Revert the Dataset
- LandingLens closes the pop-up windows and loads the Model you reverted to.