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Note that the explanations in this section are oversimplified. While we try to capture the intuition behind model outputs and model performance metrics, every object detection algorithm works differently and details differ. If you have any questions, Contact Us!

Interpreting the Graphs

Once model training is in progress, you can click on the relevant model on the training page to view training progress. There, you will see graphs monitoring training progress and model performance.

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A training step is considered each time model weights are updated, which occurs after every batch of training data is run through the model. Thus, for those more familiar with epochs, the number of training steps can be computed as Number of Epochs * (Dataset Size/Batch Size) = Number of Steps

The example above shows a graph of various losses as training progresses. The x-axis represents the number of training steps that the model has trained for, while y-axis represents the loss.

The orange line represents the loss for the training set, and the blue line represents the loss for the test set. This train-test split was selected in the Module : Dataset of the workflow. It splits the model into a train set that will be used to train the model, and a test set used to evaluate the model. Metrics for the train dataset are calculated every epoch, while metrics for the test set are calculated every set number of training steps which can be set during the workflow.

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Model Performance Metrics

Metrics used to measure model performance can be categorized as:

Output of Object Detection Algorithms

To interpret model performance, we need to first understand the output of object detection algorithms.

Given an image, object detection algorithms detect 0 or more objects. Each of these detected objects is denoted by:

1. A bounding box indicating the coordinates of the object
2. Class of object detected. Only objects present in the training set can be detected
3. (Only for Mask RCNN) A per-pixel mask predicting whether each pixel belongs to this object

The model performance metrics for General Loss , Detection Boxes, and Masks are calculated based on how the predicted objects deviate from the respective ground truth objects, based on the discrepancy in bounding box coordinates, object class, and per-pixel masks.

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General Loss Metrics

There are 4 general metrics used to measure loss across all models: Total Loss, Classification Loss, Localization Loss,, Regularization Loss.

Comparing Losses

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Losses can only be compared across models with the same parent algorithm and the same input size (e.g. RetinaResNet50 640*640 vs Retina MobileNetV2 640*640).

Losses of models from different parent algorithms (e.g. RetinaNet vs FasterRCNN) cannot be compared, as different algorithms use different formulas for calculating loss.

Losses of models with the same parent algorithm, but with a different input size (e.g. RetinaResNet50 640*640 vs RetinaResNet50 1024*1024) cannot be compared, as several algorithms include the image size when calculating loss.

Total Loss

Total loss is simply the sum of all losses used, so the classification loss, localization loss, and regularization loss.

Classification Loss

Classification loss is calculated based on the deviation between the predicted object class of each predicted bounding box, and the ground truth object class in the predicted bounding box.

Generally speaking, if a predicted bounding box has a large overlap with a ground truth bounding box, the predicted class should be the same as the object class of the ground truth bounding box (Assuming there is no other better ground truth overlap).

There might be cases where the predicted bounding box has no significant overlaps with any ground truth bounding box. Different algorithms handle this scenario differently. Some algorithms handle this by setting the ground truth class of such bounding boxes to be "background", meaning the bounding box does not contain any valid objects.

The image below shows an example object detection prediction. There is no classification loss incurred on the Cat prediction, as the ground truth is indeed a cat. There will be a classification loss incurred on the Dog prediction, as it should be predicted as background (depending on the specific algorithm).

Ideally, the predicted class of objects in predicted bounding boxes should be the same as the ground truth. This will lead to a low classification loss.

Classification loss should reduce on the train set (the orange line) as training progresses.

Localization Loss

Localization loss (or box loss) is calculated based on the deviation between the coordinates of each predicted bounding box, and the ground truth bounding box.

Generally speaking, each predicted bounding box is mapped to a ground truth bounding box based on the overlap between predicted and ground-truth bounding boxes.

The image below shows an example object detection prediction. There is some localization loss incurred on the Cat prediction, as the predicted bounding box does not capture the ground truth bounding box (the whole cat). In general, there will be no localization loss incurred on the Dog prediction, as the predicted class is wrong (this will depend on the specific algorithm).

Ideally, predicted bounding boxes should have a high overlap with their associated ground truth bounding box. This will lead to a low localization loss.

Localization loss should reduce on the train set (the orange line) as training progresses.

Regularization Loss

In Machine Learning models, regularization penalizes the weights of model coefficients, by adding an additional penalty term in the error function, proportional to the size of these penalized coefficients. This shrinks the penalized weights towards 0.

For neural network-based models, the weights of the connections between neurons can be penalized, leading to several benefits, including reducing overfitting.

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Detection Box Metrics

Comparing Detection Box Performance

Detection box metrics are all model agnostic. For a dataset with the same split for training set and evaluation set, these metrics can be compared across different models, regardless of the parent algorithm, model type, or image size.

Average Precision and Recall

We provide 6 metrics related to Average Precision, and 6 metrics related to Recall.

These 12 metrics are the same evaluation metrics used in the COCO object detection task, which is an annual object detection challenge on the COCO dataset.

Amongst the detection box metrics, Precision/mAP is calculated in a robust manner, and is generally a good metric to use in evaluating model performance. It is also the metric used in the COCO object detection task to rank models.

Class-Based Metrics

Class Metrics provide a detailed breakdown of the model's performance across different classes, helping to identify specific areas where the model is underperforming. By focusing on these class-level insights, you can address imbalances or weaknesses in certain categories that may be hidden by overall performance metrics. This approach ensures that the model performs consistently well across all classes, which is especially important in real-world applications where success often depends on the model’s ability to handle minority or challenging classes effectively.

You can filter individual class metrics by selecting specific classes when viewing the evaluation metrics.

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Mask Metrics

Mask metrics apply only to Mask RCNN models.

To understand these mask metrics, we briefly look at how Mask RCNN models work:

1. Given an image, a Region Proposal Network (RPN) generates potential bounding boxes for objects (independent of object class)
2. The bounding boxes predicted as being most likely to contain objects are selected
3. The model splits into 2 parts here. For each bounding box,
   3.1. the first part predicts the object in the bounding box, and further refines the bounding box coordinates around that object
   3.2. the second part predicts, for each possible object class in the bounding box, whether each pixel is part of that object. An example is given below

There are 3 mask metrics used to measure loss — Mask Loss, Mask RPN Localization Loss, Mask RPN Objectness Loss.

Mask Loss

For each proposed region (from RPN) that contains an object, the ground-truth pixels for that object are compared with the predicted pixels for that object by the model. For example, if the proposed region contains a pen, the ground-truth pixels containing the pen are compared with the model's predictions for pixels containing a pen.

The higher the overlap between the ground-truth pixels and predicted pixels, the lower the mask loss.

Mask RPN Localization Loss

In Step 1 mentioned above, the top bounding boxes generated by the RPN are selected.

Some of these selected bounding boxes will indeed contain an object. The RPN Localization Loss measures how closely the selected bounding boxes match the ground-truth bounding boxes of the objects contained in the selected bounding boxes.

The higher the overlap between the ground-truth bounding boxes and predicted bounding boxes, the lower the

Mask RPN Objectness Loss

In Step 1 mentioned above, the top bounding boxes generated by the RPN are selected.

For each bounding box, the RPN also predicts a probability that the bounding box contains an object (independent of object class). The Mask RPN Objectness Loss measures how accurate these predictions are.

In an ideal scenario with low Mask RPN Objectness Loss, bounding boxes with a high predicted probability of containing an object will indeed contain an object, and vice-versa.