- 18 Mar 2024
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Model Settings and Reports
- Updated on 18 Mar 2024
- 3 Minutes to read
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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.
- Select the model from the Model drop-down menu.
- Click the Actions icon (...) next to the model name.
- 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:
- Open the project.
- Click Models at the top of the page.
- Select the model you want to rename from the Model drop-down menu.
- Click the Actions icon (...) next to the model name.
- Click the Edit (pencil) icon next to the model name and enter a new name.
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. 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.
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.
- Select the model that has the dataset you're interested in from the Modeldrop-down menu.
- Click the Actions icon (...) next to the model name.
- Click the Dataset Snapshottab.
- Click the Actions icon (...) and select Download 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} |
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.