Training metrics
To view your project’s training metrics, go to Neural Network and select the training model. Select Download to download the training metrics.
Instance segmentation for Pixel Projects and Vector Projects
Metric | Description | Possible range |
|---|---|---|
Total loss | The current value of the loss function as described in Mask RCNN, a paper by Kalming He et. al. | Any range |
Mask loss | The current value of the component of the total loss function that is responsible for the mask head. | Any range |
Classes loss | The current value of the component of the total loss function that is responsible for the classes head. | Any range |
Bounding box loss | The current value of the component of the total loss function that is responsible for the bounding box head. | Any range |
Bounding box mAP | The mean average precision of the bounding boxes over all the training classes evaluated on the validation set. | 0-100 |
Bounding box mAP at IoU=0.50 | The mean average precision of bounding boxes at IoU=0.5 evaluated on the validation set. | 0-100 (per each IoU) |
Bounding box mAP at IoU=0.75 | The mean average precision of bounding boxes at IoU=0.75 evaluated on the validation set. | 0-100 (per each IoU) |
Bounding box mAP for | The average precision of bounding boxes found for particular class, i.e., how precise the model was for a given class. It is reported for all the training classes. | 0-100 |
Segmentation mAP | The mean average precision of the segmentation over all the training classes. | 0-100 |
Segmentation mAP at IoU=0.50 | The mean average precision of segmentation at IoU=0.5 | 0-100 |
Segmentation mAP at IoU=0.75 | The mean average precision of segmentation at IoU=0.75 | 0-100 |
Segmentation mAP for | The average precision of the segmentation found for a particular class, i.e., how precise the model was for a given class. | 0-100 |
ETA | Estimated time of arrival |
Object detection for Vector Projects
Metric | Description | Possible range |
|---|---|---|
Total loss | The current value of the loss function as described in Mask RCNN, a paper by Kalming He et. al. | Any range |
Classes loss | The current value of the component of the total loss function that is responsible for the classes head. | Any range |
Bounding box loss | The current value of the component of the total loss function that is responsible for the bounding box head. | Any range |
Bounding box mAP | The mean average precision of the bounding boxes over all the training classes evaluated on the validation set. | 0-100 |
Bounding box mAP at IoU=0.50 | The mean average precision of the bounding boxes over all the training classes at IoU=0.50 evaluated on the validation set. | 0-100 (per each IoU) |
Bounding box mAP at IoU=0.75 | The mean average precision of the bounding boxes over all the training classes at IoU=0.75 evaluated on the validation set. | 0-100 (per each IoU) |
Bounding box mAP for | The average precision of the bounding boxes found for a particular class, i.e., how precise the model was for a given class. It is reported for all the training classes. | 0-100 |
ETA | Estimated time of arrival |
Semantic Segmentation for Pixel Projects
Metric | Description | Possible range |
|---|---|---|
Semantic segmentation loss | Pixel-wise cross entropy loss | Any value |
ETA | Estimated time of arrival | Any value |
mIOU | Mean intersection over union over all the classes | 0-100 |
mACC | Mean accuracy over all the classes | 0-100 |
ACC | Accuracy per class | 0-100 |
Updated about 1 month ago