LandingLens | LandingLens on Snowflake |
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# | Item | Description |
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1 | Model List | Click the Model List button to show/hide the model tiles. |
2 | Name | The model name. |
3 | Performance scores for splits | The performance score for each split. Object Detection, Classification, and Anomaly Detection projects show the F1 score. Segmentation projects show the Intersection over Union (IoU) score. |
4 | More Actions | Click the Actions icon (…) to access these tools or shortcuts: Download CSV, View on Models Page, Go to Snapshot Page. |
5 | Predictions | The number of times the model made each of these predictions: False Positive, False Negative, Misclassified, and Correct. (Some predictions aren’t applicable to certain project types.) For Segmentation projects, the number is the number of pixels. |
6 | View Confusion Matrix | Click View Confusion Matrix to see the model performance metrics and confusion matrix. The data is based on the dataset that the model was trained on. |
7 | Try Model | Click Try Model to see how the model performs on new images. |
8 | Collapse and expand tile | Click to show/hide the predictions. |
9 | Load more models | Click the Load button to show more model tiles. |
Item | Description | Example |
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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 it didn’t match, the value is false. Only for 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 for Object Detection projects. | {"Screw":76060} |
GT-PRED JSON | The JSON output comparing the original labels (ground truth or “GT”) to the model’s predictions. See the JSON Output link for details. | {"gtDefectName":"No Fire","predDefectName":"No Fire","predConfidence":0.9684047} |
THRESHOLD | The confidence threshold for the model applied to the dataset. This column is only included when downloading the CSV for select images. | 0.09 |