Evaluating Models

FiftyOne provides a variety of builtin methods for evaluating your model predictions, including regressions, classifications, detections, polygons, instance and semantic segmentations, on both image and video datasets.

Note

Did you know? You can evaluate models from within the FiftyOne App by installing the @voxel51/evaluation plugin!

When you evaluate a model in FiftyOne, you get access to the standard aggregate metrics such as classification reports, confusion matrices, and PR curves for your model. In addition, FiftyOne can also record fine-grained statistics like accuracy and false positive counts at the sample-level, which you can leverage via dataset views and the FiftyOne App to interactively explore the strengths and weaknesses of your models on individual data samples.

Sample-level analysis often leads to critical insights that will help you improve your datasets and models. For example, viewing the samples with the most false positive predictions can reveal errors in your annotation schema. Or, viewing the cluster of samples with the lowest accuracy can reveal gaps in your training dataset that you need to address in order to improve your model’s performance. A key goal of FiftyOne is to help you uncover these insights on your data!

Note

Check out the tutorials page for in-depth walkthroughs of evaluating various types of models with FiftyOne.

Overview

FiftyOne’s evaluation methods are conveniently exposed as methods on all Dataset and DatasetView objects, which means that you can evaluate entire datasets or specific views into them via the same syntax.

Let’s illustrate the basic workflow by loading the quickstart dataset and analyzing the object detections in its predictions field using the evaluate_detections() method:

import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset("quickstart")
print(dataset)

# Evaluate the objects in the `predictions` field with respect to the
# objects in the `ground_truth` field
results = dataset.evaluate_detections(
    "predictions",
    gt_field="ground_truth",
    eval_key="eval_predictions",
)

Aggregate metrics

Running an evaluation returns an instance of a task-specific subclass of EvaluationResults that provides a handful of methods for generating aggregate statistics about your dataset.

# Get the 10 most common classes in the dataset
counts = dataset.count_values("ground_truth.detections.label")
classes = sorted(counts, key=counts.get, reverse=True)[:10]

# Print a classification report for the top-10 classes
results.print_report(classes=classes)

               precision    recall  f1-score   support

       person       0.45      0.74      0.56       783
         kite       0.55      0.72      0.62       156
          car       0.12      0.54      0.20        61
         bird       0.63      0.67      0.65       126
       carrot       0.06      0.49      0.11        47
         boat       0.05      0.24      0.08        37
    surfboard       0.10      0.43      0.17        30
traffic light       0.22      0.54      0.31        24
     airplane       0.29      0.67      0.40        24
      giraffe       0.26      0.65      0.37        23

    micro avg       0.32      0.68      0.44      1311
    macro avg       0.27      0.57      0.35      1311
 weighted avg       0.42      0.68      0.51      1311

Note

For details on micro, macro, and weighted averaging, see the sklearn.metrics documentation.

Sample metrics

In addition to standard aggregate metrics, when you pass an eval_key parameter to the evaluation routine, FiftyOne will populate helpful task-specific information about your model’s predictions on each sample, such as false negative/positive counts and per-sample accuracies.

Continuing with our example, let’s use dataset views and the FiftyOne App to leverage these sample metrics to investigate the samples with the most false positive predictions in the dataset:

import fiftyone as fo
from fiftyone import ViewField as F

# Create a view that has samples with the most false positives first, and
# only includes false positive boxes in the `predictions` field
view = (
    dataset
    .sort_by("eval_predictions_fp", reverse=True)
    .filter_labels("predictions", F("eval_predictions") == "fp")
)

# Visualize results in the App
session = fo.launch_app(view=view)

Notice anything wrong? The sample with the most false positives is a plate of carrots where the entire plate has been boxed as a single example in the ground truth while the model is generating predictions for individual carrots!

If you’re familiar with COCO format (which is recognized by evaluate_detections() by default), you’ll notice that the issue here is that the iscrowd attribute of this ground truth annotation has been incorrectly set to 0. Resolving mistakes like these will provide a much more accurate picture of the real performance of a model.

Confusion matrices

When you use evaluation methods such as evaluate_classifications() and evaluate_detections() to evaluate model predictions, the confusion matrices that you can generate by calling the plot_confusion_matrix() method are responsive plots that can be attached to App instances to interactively explore specific cases of your model’s performance.

Note

See this section for more information about interactive confusion matrices in FiftyOne.

Continuing with our example, the code block below generates a confusion matrix for our evaluation results and attaches it to the App.

In this setup, you can click on individual cells of the confusion matrix to select the corresponding ground truth and/or predicted objects in the App. For example, if you click on a diagonal cell of the confusion matrix, you will see the true positive examples of that class in the App.

Likewise, whenever you modify the Session’s view, either in the App or by programmatically setting session.view, the confusion matrix is automatically updated to show the cell counts for only those objects that are included in the current view.

# Plot confusion matrix
plot = results.plot_confusion_matrix(classes=classes)
plot.show()

# Connect to session
session.plots.attach(plot)

Managing evaluations

When you run an evaluation with an eval_key argument, the evaluation is recorded on the dataset and you can retrieve information about it later, rename it, delete it (along with any modifications to your dataset that were performed by it), and retrieve the view that you evaluated on using the following methods on your dataset:

• list_evaluations()

• get_evaluation_info()

• load_evaluation_results()

• load_evaluation_view()

• rename_evaluation()

• delete_evaluation()

The example below demonstrates the basic interface:

# List evaluations you've run on a dataset
dataset.list_evaluations()
# ['eval_predictions']

# Print information about an evaluation
print(dataset.get_evaluation_info("eval_predictions"))

# Load existing evaluation results and use them
results = dataset.load_evaluation_results("eval_predictions")
results.print_report()

# Rename the evaluation
# This will automatically rename any evaluation fields on your dataset
dataset.rename_evaluation("eval_predictions", "eval")

# Delete the evaluation
# This will remove any evaluation data that was populated on your dataset
dataset.delete_evaluation("eval")

The sections below discuss evaluating various types of predictions in more detail.

Regressions

You can use the evaluate_regressions() method to evaluate the predictions of a regression model stored in a Regression field of your dataset.

Invoking evaluate_regressions() returns a RegressionResults instance that provides a variety of methods for evaluating your model.

In addition, when you specify an eval_key parameter, helpful fields will be populated on each sample that you can leverage via the FiftyOne App to interactively explore the strengths and weaknesses of your model on individual samples.

Simple evaluation (default)

By default, evaluate_regressions() will evaluate each prediction by directly comparing its value to the associated ground truth value.

You can explicitly request that simple evaluation be used by setting the method parameter to "simple".

When you specify an eval_key parameter, a float eval_key field will be populated on each sample that records the error of that sample’s prediction with respect to its ground truth value. By default, the squared error will be computed, but you can customize this via the optional metric argument to evaluate_regressions(), which can take any value supported by SimpleEvaluationConfig.

The example below demonstrates simple evaluation on the quickstart dataset with some fake regression data added to it to demonstrate the workflow:

import random
import numpy as np

import fiftyone as fo
import fiftyone.zoo as foz
from fiftyone import ViewField as F

dataset = foz.load_zoo_dataset("quickstart").select_fields().clone()

# Populate some fake regression + weather data
for idx, sample in enumerate(dataset, 1):
    ytrue = random.random() * idx
    ypred = ytrue + np.random.randn() * np.sqrt(ytrue)
    confidence = random.random()
    sample["ground_truth"] = fo.Regression(value=ytrue)
    sample["predictions"] = fo.Regression(value=ypred, confidence=confidence)
    sample["weather"] = random.choice(["sunny", "cloudy", "rainy"])
    sample.save()

print(dataset)

# Evaluate the predictions in the `predictions` field with respect to the
# values in the `ground_truth` field
results = dataset.evaluate_regressions(
    "predictions",
    gt_field="ground_truth",
    eval_key="eval",
)

# Print some standard regression evaluation metrics
results.print_metrics()

# Plot a scatterplot of the results colored by `weather` and scaled by
# `confidence`
plot = results.plot_results(labels="weather", sizes="predictions.confidence")
plot.show()

# Launch the App to explore
session = fo.launch_app(dataset)

# Show the samples with the smallest regression error
session.view = dataset.sort_by("eval")

# Show the samples with the largest regression error
session.view = dataset.sort_by("eval", reverse=True)

mean squared error        59.69
root mean squared error   7.73
mean absolute error       5.48
median absolute error     3.57
r2 score                  0.97
explained variance score  0.97
max error                 31.77
support                   200

Note

Did you know? You can attach regression plots to the App and interactively explore them by selecting scatter points and/or modifying your view in the App.

Classifications

You can use the evaluate_classifications() method to evaluate the predictions of a classifier stored in a Classification field of your dataset.

By default, the classifications will be treated as a generic multiclass classification task, but you can specify other evaluation strategies such as top-k accuracy or binary evaluation via the method parameter.

Invoking evaluate_classifications() returns a ClassificationResults instance that provides a variety of methods for generating various aggregate evaluation reports about your model.

In addition, when you specify an eval_key parameter, a number of helpful fields will be populated on each sample that you can leverage via the FiftyOne App to interactively explore the strengths and weaknesses of your model on individual samples.

Simple evaluation (default)

By default, evaluate_classifications() will treat your classifications as generic multiclass predictions, and it will evaluate each prediction by directly comparing its label to the associated ground truth prediction.

You can explicitly request that simple evaluation be used by setting the method parameter to "simple".

When you specify an eval_key parameter, a boolean eval_key field will be populated on each sample that records whether that sample’s prediction is correct.

The example below demonstrates simple evaluation on the CIFAR-10 dataset with some fake predictions added to it to demonstrate the workflow:

import random

import fiftyone as fo
import fiftyone.zoo as foz
from fiftyone import ViewField as F

dataset = foz.load_zoo_dataset(
    "cifar10",
    split="test",
    max_samples=1000,
    shuffle=True,
)

#
# Create some test predictions by copying the ground truth labels into a
# new `predictions` field with 10% of the labels perturbed at random
#

classes = dataset.distinct("ground_truth.label")

def jitter(val):
    if random.random() < 0.10:
        return random.choice(classes)

    return val

predictions = [
    fo.Classification(label=jitter(gt.label), confidence=random.random())
    for gt in dataset.values("ground_truth")
]

dataset.set_values("predictions", predictions)

print(dataset)

# Evaluate the predictions in the `predictions` field with respect to the
# labels in the `ground_truth` field
results = dataset.evaluate_classifications(
    "predictions",
    gt_field="ground_truth",
    eval_key="eval_simple",
)

# Print a classification report
results.print_report()

# Plot a confusion matrix
plot = results.plot_confusion_matrix()
plot.show()

# Launch the App to explore
session = fo.launch_app(dataset)

# View only the incorrect predictions in the App
session.view = dataset.match(F("eval_simple") == False)

              precision    recall  f1-score   support

    airplane       0.91      0.90      0.91       118
  automobile       0.93      0.90      0.91       101
        bird       0.93      0.87      0.90       103
         cat       0.92      0.91      0.92        94
        deer       0.88      0.92      0.90       116
         dog       0.85      0.84      0.84        86
        frog       0.85      0.92      0.88        84
       horse       0.88      0.91      0.89        96
        ship       0.93      0.95      0.94        97
       truck       0.92      0.89      0.90       105

    accuracy                           0.90      1000
   macro avg       0.90      0.90      0.90      1000
weighted avg       0.90      0.90      0.90      1000

Note

Did you know? You can attach confusion matrices to the App and interactively explore them by clicking on their cells and/or modifying your view in the App.

Top-k evaluation

Set the method parameter of evaluate_classifications() to top-k in order to use top-k matching to evaluate your classifications.

Under this strategy, predictions are deemed to be correct if the corresponding ground truth label is within the top k predictions.

When you specify an eval_key parameter, a boolean eval_key field will be populated on each sample that records whether that sample’s prediction is correct.

Note

In order to use top-k evaluation, you must populate the logits field of your predictions, and you must provide the list of corresponding class labels via the classes parameter of evaluate_classifications().

Did you know? Many models from the Model Zoo provide support for storing logits for their predictions!

The example below demonstrates top-k evaluation on a small ImageNet sample with predictions from a pre-trained model from the Model Zoo:

import fiftyone as fo
import fiftyone.zoo as foz
from fiftyone import ViewField as F

dataset = foz.load_zoo_dataset(
    "imagenet-sample", dataset_name="top-k-eval-demo"
)

# We need the list of class labels corresponding to the logits
logits_classes = dataset.default_classes

# Add predictions (with logits) to 25 random samples
predictions_view = dataset.take(25, seed=51)
model = foz.load_zoo_model("resnet50-imagenet-torch")
predictions_view.apply_model(model, "predictions", store_logits=True)

print(predictions_view)

# Evaluate the predictions in the `predictions` field with respect to the
# labels in the `ground_truth` field using top-5 accuracy
results = predictions_view.evaluate_classifications(
    "predictions",
    gt_field="ground_truth",
    eval_key="eval_top_k",
    method="top-k",
    classes=logits_classes,
    k=5,
)

# Get the 10 most common classes in the view
counts = predictions_view.count_values("ground_truth.label")
classes = sorted(counts, key=counts.get, reverse=True)[:10]

# Print a classification report for the top-10 classes
results.print_report(classes=classes)

# Launch the App to explore
session = fo.launch_app(dataset)

# View only the incorrect predictions for the 10 most common classes
session.view = (
    predictions_view
    .match(F("ground_truth.label").is_in(classes))
    .match(F("eval_top_k") == False)
)

Binary evaluation

If your classifier is binary, set the method parameter of evaluate_classifications() to "binary" in order to access binary-specific evaluation information such as precision-recall curves for your model.

When you specify an eval_key parameter, a string eval_key field will be populated on each sample that records whether the sample is a true positive, false positive, true negative, or false negative.

Note

In order to use binary evaluation, you must provide the (neg_label, pos_label) for your model via the classes parameter of evaluate_classifications().

The example below demonstrates binary evaluation on the CIFAR-10 dataset with some fake binary predictions added to it to demonstrate the workflow:

import random

import fiftyone as fo
import fiftyone.zoo as foz

# Load a small sample from the ImageNet dataset
dataset = foz.load_zoo_dataset(
    "cifar10",
    split="test",
    max_samples=1000,
    shuffle=True,
)

#
# Binarize the ground truth labels to `cat` and `other`, and add
# predictions that are correct proportionally to their confidence
#

classes = ["other", "cat"]

for sample in dataset:
    gt_label = "cat" if sample.ground_truth.label == "cat" else "other"

    confidence = random.random()
    if random.random() > confidence:
        pred_label = "cat" if gt_label == "other" else "other"
    else:
        pred_label = gt_label

    sample.ground_truth.label = gt_label
    sample["predictions"] = fo.Classification(
        label=pred_label, confidence=confidence
    )

    sample.save()

print(dataset)

# Evaluate the predictions in the `predictions` field with respect to the
# labels in the `ground_truth` field
results = dataset.evaluate_classifications(
    "predictions",
    gt_field="ground_truth",
    eval_key="eval_binary",
    method="binary",
    classes=classes,
)

# Print a classification report
results.print_report()

# Plot a PR curve
plot = results.plot_pr_curve()
plot.show()

              precision    recall  f1-score   support

       other       0.90      0.48      0.63       906
         cat       0.09      0.50      0.15        94

    accuracy                           0.48      1000
   macro avg       0.50      0.49      0.39      1000
weighted avg       0.83      0.48      0.59      1000

Detections

You can use the evaluate_detections() method to evaluate the predictions of an object detection model stored in a Detections, Polylines, or Keypoints field of your dataset or of a temporal detection model stored in a TemporalDetections field of your dataset.

Invoking evaluate_detections() returns a DetectionResults instance that provides a variety of methods for generating various aggregate evaluation reports about your model.

In addition, when you specify an eval_key parameter, a number of helpful fields will be populated on each sample and its predicted/ground truth objects that you can leverage via the FiftyOne App to interactively explore the strengths and weaknesses of your model on individual samples.

Note

FiftyOne uses the COCO-style evaluation by default, but Open Images-style evaluation is also natively supported.

Supported types

The evaluate_detections() method supports all of the following task types:

• Object detection

• Instance segmentations

• Polygon detection

• Keypoints

• Temporal detections

• 3D detections

The only difference between each task type is in how the IoU between objects is calculated:

• For object detections, IoUs are computed between each pair of bounding boxes

• For instance segmentations and polygons, IoUs are computed between the polygonal shapes rather than their rectangular bounding boxes

• For keypoint tasks, object keypoint similarity is computed for each pair of objects, using the extent of the ground truth keypoints as a proxy for the area of the object’s bounding box, and assuming uniform falloff (\(\kappa\))

• For temporal detections, IoU is computed between the 1D support of two temporal segments

For object detection tasks, the ground truth and predicted objects should be stored in Detections format.

For instance segmentation tasks, the ground truth and predicted objects should be stored in Detections format, and each Detection instance should have its mask attribute populated to define the extent of the object within its bounding box.

Note

In order to use instance masks for IoU calculations, pass use_masks=True to evaluate_detections().

For polygon detection tasks, the ground truth and predicted objects should be stored in Polylines format with their filled attribute set to True to indicate that they represent closed polygons (as opposed to polylines).

Note

If you are evaluating polygons but would rather use bounding boxes rather than the actual polygonal geometries for IoU calculations, you can pass use_boxes=True to evaluate_detections().

For keypoint tasks, each Keypoint instance must contain point arrays of equal length and semantic ordering.

Note

If a particular point is missing or not visible for a Keypoint instance, use nan values for its coordinates. See here for more information about structuring keypoints.

For temporal detection tasks, the ground truth and predicted objects should be stored in TemporalDetections format.

Evaluation patches views

Once you have run evaluate_detections() on a dataset, you can use to_evaluation_patches() to transform the dataset (or a view into it) into a new view that contains one sample for each true positive, false positive, and false negative example.

True positive examples will result in samples with both their ground truth and predicted fields populated, while false positive/negative examples will only have one of their corresponding predicted/ground truth fields populated, respectively.

If multiple predictions are matched to a ground truth object (e.g., if the evaluation protocol includes a crowd attribute), then all matched predictions will be stored in the single sample along with the ground truth object.

Evaluation patches views also have top-level type and iou fields populated based on the evaluation results for that example, as well as a sample_id field recording the sample ID of the example, and a crowd field if the evaluation protocol defines a crowd attribute.

Note

Evaluation patches views generate patches for only the contents of the current view, which may differ from the view on which the eval_key evaluation was performed. This may exclude some labels that were evaluated and/or include labels that were not evaluated.

If you would like to see patches for the exact view on which an evaluation was performed, first call load_evaluation_view() to load the view and then convert to patches.

The example below demonstrates loading an evaluation patches view for the results of an evaluation on the quickstart dataset:

import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset("quickstart")

# Evaluate `predictions` w.r.t. labels in `ground_truth` field
dataset.evaluate_detections(
    "predictions", gt_field="ground_truth", eval_key="eval"
)

session = fo.launch_app(dataset)

# Convert to evaluation patches
eval_patches = dataset.to_evaluation_patches("eval")
print(eval_patches)

print(eval_patches.count_values("type"))
# {'fn': 246, 'fp': 4131, 'tp': 986}

# View patches in the App
session.view = eval_patches

Dataset:     quickstart
Media type:  image
Num patches: 5363
Patch fields:
    filepath:     fiftyone.core.fields.StringField
    tags:         fiftyone.core.fields.ListField(fiftyone.core.fields.StringField)
    metadata:     fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.metadata.ImageMetadata)
    predictions:  fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)
    ground_truth: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)
    sample_id:    fiftyone.core.fields.StringField
    type:         fiftyone.core.fields.StringField
    iou:          fiftyone.core.fields.FloatField
    crowd:        fiftyone.core.fields.BooleanField
View stages:
    1. ToEvaluationPatches(eval_key='eval', config=None)

Note

Did you know? You can convert to evaluation patches view directly from the App!

Evaluation patches views are just like any other dataset view in the sense that:

• You can append view stages via the App view bar or views API

• Any modifications to ground truth or predicted label tags that you make via the App’s tagging menu or via API methods like tag_labels() and untag_labels() will be reflected on the source dataset

• Any modifications to the predicted or ground truth Label elements in the patches view that you make by iterating over the contents of the view or calling set_values() will be reflected on the source dataset

• Calling save() on an evaluation patches view (typically one that contains additional view stages that filter or modify its contents) will sync any Label edits or deletions with the source dataset

However, because evaluation patches views only contain a subset of the contents of a Sample from the source dataset, there are some differences in behavior compared to non-patch views:

• Tagging or untagging patches themselves (as opposed to their labels) will not affect the tags of the underlying Sample

• Any new fields that you add to an evaluation patches view will not be added to the source dataset

COCO-style evaluation (default spatial)

By default, evaluate_detections() will use COCO-style evaluation to analyze predictions when the specified label fields are Detections or Polylines.

You can also explicitly request that COCO-style evaluation be used by setting the method parameter to "coco".

Note

FiftyOne’s implementation of COCO-style evaluation matches the reference implementation available via pycocotools.

Overview

When running COCO-style evaluation using evaluate_detections():

• Predicted and ground truth objects are matched using a specified IoU threshold (default = 0.50). This threshold can be customized via the iou parameter

• By default, only objects with the same label will be matched. Classwise matching can be disabled via the classwise parameter

• Ground truth objects can have an iscrowd attribute that indicates whether the annotation contains a crowd of objects. Multiple predictions can be matched to crowd ground truth objects. The name of this attribute can be customized by passing the optional iscrowd attribute of COCOEvaluationConfig to evaluate_detections()

When you specify an eval_key parameter, a number of helpful fields will be populated on each sample and its predicted/ground truth objects:

• True positive (TP), false positive (FP), and false negative (FN) counts for the each sample are saved in top-level fields of each sample:

  TP: sample.<eval_key>_tp
  FP: sample.<eval_key>_fp
  FN: sample.<eval_key>_fn
  

• The fields listed below are populated on each individual object instance; these fields tabulate the TP/FP/FN status of the object, the ID of the matching object (if any), and the matching IoU:

  TP/FP/FN: object.<eval_key>
        ID: object.<eval_key>_id
       IoU: object.<eval_key>_iou
  

Note

See COCOEvaluationConfig for complete descriptions of the optional keyword arguments that you can pass to evaluate_detections() when running COCO-style evaluation.

Example evaluation

The example below demonstrates COCO-style detection evaluation on the quickstart dataset:

import fiftyone as fo
import fiftyone.zoo as foz
from fiftyone import ViewField as F

dataset = foz.load_zoo_dataset("quickstart")
print(dataset)

# Evaluate the objects in the `predictions` field with respect to the
# objects in the `ground_truth` field
results = dataset.evaluate_detections(
    "predictions",
    gt_field="ground_truth",
    eval_key="eval",
)

# Get the 10 most common classes in the dataset
counts = dataset.count_values("ground_truth.detections.label")
classes = sorted(counts, key=counts.get, reverse=True)[:10]

# Print a classification report for the top-10 classes
results.print_report(classes=classes)

# Print some statistics about the total TP/FP/FN counts
print("TP: %d" % dataset.sum("eval_tp"))
print("FP: %d" % dataset.sum("eval_fp"))
print("FN: %d" % dataset.sum("eval_fn"))

# Create a view that has samples with the most false positives first, and
# only includes false positive boxes in the `predictions` field
view = (
    dataset
    .sort_by("eval_fp", reverse=True)
    .filter_labels("predictions", F("eval") == "fp")
)

# Visualize results in the App
session = fo.launch_app(view=view)

               precision    recall  f1-score   support

       person       0.45      0.74      0.56       783
         kite       0.55      0.72      0.62       156
          car       0.12      0.54      0.20        61
         bird       0.63      0.67      0.65       126
       carrot       0.06      0.49      0.11        47
         boat       0.05      0.24      0.08        37
    surfboard       0.10      0.43      0.17        30
     airplane       0.29      0.67      0.40        24
traffic light       0.22      0.54      0.31        24
        bench       0.10      0.30      0.15        23

    micro avg       0.32      0.68      0.43      1311
    macro avg       0.26      0.54      0.32      1311
 weighted avg       0.42      0.68      0.50      1311

mAP and PR curves

You can compute mean average precision (mAP) and precision-recall (PR) curves for your objects by passing the compute_mAP=True flag to evaluate_detections():

Note

All mAP calculations are performed according to the COCO evaluation protocol.

import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset("quickstart")
print(dataset)

# Performs an IoU sweep so that mAP and PR curves can be computed
results = dataset.evaluate_detections(
    "predictions",
    gt_field="ground_truth",
    compute_mAP=True,
)

print(results.mAP())
# 0.3957

plot = results.plot_pr_curves(classes=["person", "kite", "car"])
plot.show()

Confusion matrices

You can also easily generate confusion matrices for the results of COCO-style evaluations.

In order for the confusion matrix to capture anything other than false positive/negative counts, you will likely want to set the classwise parameter to False during evaluation so that predicted objects can be matched with ground truth objects of different classes.

import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset("quickstart")

# Perform evaluation, allowing objects to be matched between classes
results = dataset.evaluate_detections(
    "predictions", gt_field="ground_truth", classwise=False
)

# Generate a confusion matrix for the specified classes
plot = results.plot_confusion_matrix(classes=["car", "truck", "motorcycle"])
plot.show()

Note

Did you know? Confusion matrices can be attached to your Session object and dynamically explored using FiftyOne’s interactive plotting features!

Open Images-style evaluation

The evaluate_detections() method also supports Open Images-style evaluation.

In order to run Open Images-style evaluation, simply set the method parameter to "open-images".

Note

FiftyOne’s implementation of Open Images-style evaluation matches the reference implementation available via the TF Object Detection API.

Overview

Open Images-style evaluation provides additional features not found in COCO-style evaluation that you may find useful when evaluating your custom datasets.

The two primary differences are:

• Non-exhaustive image labeling: positive and negative sample-level Classifications fields can be provided to indicate which object classes were considered when annotating the image. Predicted objects whose classes are not included in the sample-level labels for a sample are ignored. The names of these fields can be specified via the pos_label_field and neg_label_field parameters

• Class hierarchies: If your dataset includes a class hierarchy, you can configure this evaluation protocol to automatically expand ground truth and/or predicted leaf classes so that all levels of the hierarchy can be correctly evaluated. You can provide a label hierarchy via the hierarchy parameter. By default, if you provide a hierarchy, then image-level label fields and ground truth detections will be expanded to incorporate parent classes (child classes for negative image-level labels). You can disable this feature by setting the expand_gt_hierarchy parameter to False. Alternatively, you can expand predictions by setting the expand_pred_hierarchy parameter to True

In addition, note that:

• Like VOC-style evaluation, only one IoU (default = 0.5) is used to calculate mAP. You can customize this value via the iou parameter

• When dealing with crowd objects, Open Images-style evaluation dictates that if a crowd is matched with multiple predictions, each counts as one true positive when computing mAP

When you specify an eval_key parameter, a number of helpful fields will be populated on each sample and its predicted/ground truth objects:

• True positive (TP), false positive (FP), and false negative (FN) counts for the each sample are saved in top-level fields of each sample:

  TP: sample.<eval_key>_tp
  FP: sample.<eval_key>_fp
  FN: sample.<eval_key>_fn
  

• The fields listed below are populated on each individual Detection instance; these fields tabulate the TP/FP/FN status of the object, the ID of the matching object (if any), and the matching IoU:

  TP/FP/FN: object.<eval_key>
        ID: object.<eval_key>_id
       IoU: object.<eval_key>_iou
  

Note

See OpenImagesEvaluationConfig for complete descriptions of the optional keyword arguments that you can pass to evaluate_detections() when running Open Images-style evaluation.

Example evaluation

The example below demonstrates Open Images-style detection evaluation on the quickstart dataset:

import fiftyone as fo
import fiftyone.zoo as foz
from fiftyone import ViewField as F

dataset = foz.load_zoo_dataset("quickstart")
print(dataset)

# Evaluate the objects in the `predictions` field with respect to the
# objects in the `ground_truth` field
results = dataset.evaluate_detections(
    "predictions",
    gt_field="ground_truth",
    method="open-images",
    eval_key="eval",
)

# Get the 10 most common classes in the dataset
counts = dataset.count_values("ground_truth.detections.label")
classes = sorted(counts, key=counts.get, reverse=True)[:10]

# Print a classification report for the top-10 classes
results.print_report(classes=classes)

# Print some statistics about the total TP/FP/FN counts
print("TP: %d" % dataset.sum("eval_tp"))
print("FP: %d" % dataset.sum("eval_fp"))
print("FN: %d" % dataset.sum("eval_fn"))

# Create a view that has samples with the most false positives first, and
# only includes false positive boxes in the `predictions` field
view = (
    dataset
    .sort_by("eval_fp", reverse=True)
    .filter_labels("predictions", F("eval") == "fp")
)

# Visualize results in the App
session = fo.launch_app(view=view)

               precision    recall  f1-score   support

       person       0.25      0.86      0.39       378
         kite       0.27      0.75      0.40        75
          car       0.18      0.80      0.29        61
         bird       0.20      0.51      0.28        51
       carrot       0.09      0.74      0.16        47
         boat       0.09      0.46      0.16        37
    surfboard       0.17      0.73      0.28        30
     airplane       0.36      0.83      0.50        24
traffic light       0.32      0.79      0.45        24
      giraffe       0.36      0.91      0.52        23

    micro avg       0.21      0.79      0.34       750
    macro avg       0.23      0.74      0.34       750
 weighted avg       0.23      0.79      0.36       750

mAP and PR curves

You can easily compute mean average precision (mAP) and precision-recall (PR) curves using the results object returned by evaluate_detections():

Note

FiftyOne’s implementation of Open Images-style evaluation matches the reference implementation available via the TF Object Detection API.

import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset("quickstart")
print(dataset)

results = dataset.evaluate_detections(
    "predictions",
    gt_field="ground_truth",
    method="open-images",
)

print(results.mAP())
# 0.599

plot = results.plot_pr_curves(classes=["person", "dog", "car"])
plot.show()

Confusion matrices

You can also easily generate confusion matrices for the results of Open Images-style evaluations.

In order for the confusion matrix to capture anything other than false positive/negative counts, you will likely want to set the classwise parameter to False during evaluation so that predicted objects can be matched with ground truth objects of different classes.

import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset("quickstart")

# Perform evaluation, allowing objects to be matched between classes
results = dataset.evaluate_detections(
    "predictions",
    gt_field="ground_truth",
    method="open-images",
    classwise=False,
)

# Generate a confusion matrix for the specified classes
plot = results.plot_confusion_matrix(classes=["car", "truck", "motorcycle"])
plot.show()

Note

Did you know? Confusion matrices can be attached to your Session object and dynamically explored using FiftyOne’s interactive plotting features!

ActivityNet-style evaluation (default temporal)

By default, evaluate_detections() will use ActivityNet-style temporal detection evaluation. to analyze predictions when the specified label fields are TemporalDetections.

You can also explicitly request that ActivityNet-style evaluation be used by setting the method parameter to "activitynet".

Note

FiftyOne’s implementation of ActivityNet-style evaluation matches the reference implementation available via the ActivityNet API.

Overview

When running ActivityNet-style evaluation using evaluate_detections():

• Predicted and ground truth segments are matched using a specified IoU threshold (default = 0.50). This threshold can be customized via the iou parameter

• By default, only segments with the same label will be matched. Classwise matching can be disabled by passing classwise=False

• mAP is computed by averaging over the same range of IoU values used by COCO

When you specify an eval_key parameter, a number of helpful fields will be populated on each sample and its predicted/ground truth segments:

• True positive (TP), false positive (FP), and false negative (FN) counts for the each sample are saved in top-level fields of each sample:

  TP: sample.<eval_key>_tp
  FP: sample.<eval_key>_fp
  FN: sample.<eval_key>_fn
  

• The fields listed below are populated on each individual temporal detection segment; these fields tabulate the TP/FP/FN status of the segment, the ID of the matching segment (if any), and the matching IoU:

  TP/FP/FN: segment.<eval_key>
        ID: segment.<eval_key>_id
       IoU: segment.<eval_key>_iou
  

Note

See ActivityNetEvaluationConfig for complete descriptions of the optional keyword arguments that you can pass to evaluate_detections() when running ActivityNet-style evaluation.

Example evaluation

The example below demonstrates ActivityNet-style temporal detection evaluation on the ActivityNet 200 dataset:

import fiftyone as fo
import fiftyone.zoo as foz
from fiftyone import ViewField as F

import random

# Load subset of ActivityNet 200
classes = ["Bathing dog", "Walking the dog"]
dataset = foz.load_zoo_dataset(
    "activitynet-200",
    split="validation",
    classes=classes,
    max_samples=10,
)
print(dataset)

# Generate some fake predictions for this example
random.seed(51)
dataset.clone_sample_field("ground_truth", "predictions")
for sample in dataset:
    for det in sample.predictions.detections:
        det.support[0] += random.randint(-10,10)
        det.support[1] += random.randint(-10,10)
        det.support[0] = max(det.support[0], 1)
        det.support[1] = max(det.support[1], det.support[0] + 1)
        det.confidence = random.random()
        det.label = random.choice(classes)

    sample.save()

# Evaluate the segments in the `predictions` field with respect to the
# segments in the `ground_truth` field
results = dataset.evaluate_detections(
    "predictions",
    gt_field="ground_truth",
    eval_key="eval",
)

# Print a classification report for the classes
results.print_report(classes=classes)

# Print some statistics about the total TP/FP/FN counts
print("TP: %d" % dataset.sum("eval_tp"))
print("FP: %d" % dataset.sum("eval_fp"))
print("FN: %d" % dataset.sum("eval_fn"))

# Create a view that has samples with the most false positives first, and
# only includes false positive segments in the `predictions` field
view = (
    dataset
    .sort_by("eval_fp", reverse=True)
    .filter_labels("predictions", F("eval") == "fp")
)

# Visualize results in the App
session = fo.launch_app(view=view)

                 precision    recall  f1-score   support

    Bathing dog       0.50      0.40      0.44         5
Walking the dog       0.50      0.60      0.55         5

      micro avg       0.50      0.50      0.50        10
      macro avg       0.50      0.50      0.49        10
   weighted avg       0.50      0.50      0.49        10

mAP and PR curves

You can compute mean average precision (mAP) and precision-recall (PR) curves for your segments by passing the compute_mAP=True flag to evaluate_detections():

Note

All mAP calculations are performed according to the ActivityNet evaluation protocol.

import random

import fiftyone as fo
import fiftyone.zoo as foz

# Load subset of ActivityNet 200
classes = ["Bathing dog", "Walking the dog"]
dataset = foz.load_zoo_dataset(
    "activitynet-200",
    split="validation",
    classes=classes,
    max_samples=10,
)
print(dataset)

# Generate some fake predictions for this example
random.seed(51)
dataset.clone_sample_field("ground_truth", "predictions")
for sample in dataset:
    for det in sample.predictions.detections:
        det.support[0] += random.randint(-10,10)
        det.support[1] += random.randint(-10,10)
        det.support[0] = max(det.support[0], 1)
        det.support[1] = max(det.support[1], det.support[0] + 1)
        det.confidence = random.random()
        det.label = random.choice(classes)

    sample.save()

# Performs an IoU sweep so that mAP and PR curves can be computed
results = dataset.evaluate_detections(
    "predictions",
    gt_field="ground_truth",
    eval_key="eval",
    compute_mAP=True,
)

print(results.mAP())
# 0.367

plot = results.plot_pr_curves(classes=classes)
plot.show()

Confusion matrices

You can also easily generate confusion matrices for the results of ActivityNet-style evaluations.

In order for the confusion matrix to capture anything other than false positive/negative counts, you will likely want to set the classwise parameter to False during evaluation so that predicted segments can be matched with ground truth segments of different classes.

import random

import fiftyone as fo
import fiftyone.zoo as foz

# Load subset of ActivityNet 200
classes = ["Bathing dog", "Grooming dog", "Grooming horse", "Walking the dog"]
dataset = foz.load_zoo_dataset(
    "activitynet-200",
    split="validation",
    classes=classes,
    max_samples=20,
)
print(dataset)

# Generate some fake predictions for this example
random.seed(51)
dataset.clone_sample_field("ground_truth", "predictions")
for sample in dataset:
    for det in sample.predictions.detections:
        det.support[0] += random.randint(-10,10)
        det.support[1] += random.randint(-10,10)
        det.support[0] = max(det.support[0], 1)
        det.support[1] = max(det.support[1], det.support[0] + 1)
        det.confidence = random.random()
        det.label = random.choice(classes)

    sample.save()

# Perform evaluation, allowing objects to be matched between classes
results = dataset.evaluate_detections(
    "predictions", gt_field="ground_truth", classwise=False
)

# Generate a confusion matrix for the specified classes
plot = results.plot_confusion_matrix(classes=classes)
plot.show()

Note

Did you know? Confusion matrices can be attached to your Session object and dynamically explored using FiftyOne’s interactive plotting features!

Semantic segmentations

You can use the evaluate_segmentations() method to evaluate the predictions of a semantic segmentation model stored in a Segmentation field of your dataset.

By default, the full segmentation masks will be evaluated at a pixel level, but you can specify other evaluation strategies such as evaluating only boundary pixels (see below for details).

Invoking evaluate_segmentations() returns a SegmentationResults instance that provides a variety of methods for generating various aggregate evaluation reports about your model.

In addition, when you specify an eval_key parameter, a number of helpful fields will be populated on each sample that you can leverage via the FiftyOne App to interactively explore the strengths and weaknesses of your model on individual samples.

Note

You can store mask targets for your Segmentation fields on your dataset so that you can view semantic labels in the App and avoid having to manually specify the set of mask targets each time you run evaluate_segmentations() on a dataset.

Simple evaluation (default)

By default, evaluate_segmentations() will perform pixelwise evaluation of the segmentation masks, treating each pixel as a multiclass classification.

Here are some things to keep in mind:

• If the size of a predicted mask does not match the ground truth mask, it is resized to match the ground truth.

• You can specify the optional bandwidth parameter to evaluate only along the contours of the ground truth masks. By default, the entire masks are evaluated.

You can explicitly request that this strategy be used by setting the method parameter to "simple".

When you specify an eval_key parameter, the accuracy, precision, and recall of each sample is recorded in top-level fields of each sample:

 Accuracy: sample.<eval_key>_accuracy
Precision: sample.<eval_key>_precision
   Recall: sample.<eval_key>_recall

Note

The mask values 0 and #000000 are treated as a background class for the purposes of computing evaluation metrics like precision and recall.

The example below demonstrates segmentation evaluation by comparing the masks generated by two DeepLabv3 models (with ResNet50 and ResNet101 backbones):

import fiftyone as fo
import fiftyone.zoo as foz

# Load a few samples from COCO-2017
dataset = foz.load_zoo_dataset(
    "quickstart",
    dataset_name="segmentation-eval-demo",
    max_samples=10,
    shuffle=True,
)

# The models are trained on the VOC classes
CLASSES = (
    "background,aeroplane,bicycle,bird,boat,bottle,bus,car,cat,chair,cow," +
    "diningtable,dog,horse,motorbike,person,pottedplant,sheep,sofa,train," +
    "tvmonitor"
)
dataset.default_mask_targets = {
    idx: label for idx, label in enumerate(CLASSES.split(","))
}

# Add DeepLabv3-ResNet101 predictions to dataset
model = foz.load_zoo_model("deeplabv3-resnet101-coco-torch")
dataset.apply_model(model, "resnet101")

# Add DeepLabv3-ResNet50 predictions to dataset
model = foz.load_zoo_model("deeplabv3-resnet50-coco-torch")
dataset.apply_model(model, "resnet50")

print(dataset)

# Evaluate the masks w/ ResNet50 backbone, treating the masks w/ ResNet101
# backbone as "ground truth"
results = dataset.evaluate_segmentations(
    "resnet50",
    gt_field="resnet101",
    eval_key="eval_simple",
)

# Get a sense for the per-sample variation in likeness
print("Accuracy range: (%f, %f)" % dataset.bounds("eval_simple_accuracy"))
print("Precision range: (%f, %f)" % dataset.bounds("eval_simple_precision"))
print("Recall range: (%f, %f)" % dataset.bounds("eval_simple_recall"))

# Print a classification report
results.print_report()

# Visualize results in the App
session = fo.launch_app(dataset)

Advanced usage

Evaluating views into your dataset

All evaluation methods are exposed on DatasetView objects, which means that you can define arbitrarily complex views into your datasets and run evaluation on those.

For example, the snippet below evaluates only the medium-sized objects in a dataset:

import fiftyone as fo
import fiftyone.zoo as foz
from fiftyone import ViewField as F

dataset = foz.load_zoo_dataset("quickstart", dataset_name="eval-demo")
dataset.compute_metadata()

# Create an expression that will match objects whose bounding boxes have
# areas between 32^2 and 96^2 pixels
bbox_area = (
    F("$metadata.width") * F("bounding_box")[2] *
    F("$metadata.height") * F("bounding_box")[3]
)
medium_boxes = (32 ** 2 < bbox_area) & (bbox_area < 96 ** 2)

# Create a view that contains only medium-sized objects
medium_view = (
    dataset
    .filter_labels("ground_truth", medium_boxes)
    .filter_labels("predictions", medium_boxes)
)

print(medium_view)

# Evaluate the medium-sized objects
results = medium_view.evaluate_detections(
    "predictions",
    gt_field="ground_truth",
    eval_key="eval_medium",
)

# Print some aggregate metrics
print(results.metrics())

# View results in the App
session = fo.launch_app(view=medium_view)

Note

If you run evaluation on a complex view, don’t worry, you can always load the view later!

Loading a previous evaluation result

You can view a list of evaluation keys for evaluations that you have previously run on a dataset via list_evaluations().

Evaluation keys are stored at the dataset-level, but if a particular evaluation was run on a view into your dataset, you can use load_evaluation_view() to retrieve the exact view on which you evaluated:

import fiftyone as fo

dataset = fo.load_dataset(...)

# List available evaluations
dataset.list_evaluations()
# ["my_eval1", "my_eval2", ...]

# Load the view into the dataset on which `my_eval1` was run
eval1_view = dataset.load_evaluation_view("my_eval1")

Note

If you have run multiple evaluations on a dataset, you can use the select_fields parameter of the load_evaluation_view() method to hide any fields that were populated by other evaluation runs, allowing you to, for example, focus on a specific set of evaluation results in the App:

import fiftyone as fo

dataset = fo.load_dataset(...)

# Load a view that contains the results of evaluation `my_eval1` and
# hides all other evaluation data
eval1_view = dataset.load_evaluation_view("my_eval1", select_fields=True)

session = fo.launch_app(view=eval1_view)

Evaluating videos

All evaluation methods can be applied to frame-level labels in addition to sample-level labels.

You can evaluate frame-level labels of a video dataset by adding the frames prefix to the relevant prediction and ground truth frame fields.

Note

When evaluating frame-level labels, helpful statistics are tabulated at both the sample- and frame-levels of your dataset. Refer to the documentation of the relevant evaluation method for more details.

The example below demonstrates evaluating (mocked) frame-level detections on the quickstart-video dataset from the Dataset Zoo:

import random

import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "quickstart-video", dataset_name="video-eval-demo"
)

#
# Create some test predictions by copying the ground truth objects into a
# new `predictions` field of the frames with 10% of the labels perturbed at
# random
#

classes = dataset.distinct("frames.detections.detections.label")

def jitter(val):
    if random.random() < 0.10:
        return random.choice(classes)

    return val

predictions = []
for sample_gts in dataset.values("frames.detections"):
    sample_predictions = []
    for frame_gts in sample_gts:
        sample_predictions.append(
            fo.Detections(
                detections=[
                    fo.Detection(
                        label=jitter(gt.label),
                        bounding_box=gt.bounding_box,
                        confidence=random.random(),
                    )
                    for gt in frame_gts.detections
                ]
            )
        )

    predictions.append(sample_predictions)

dataset.set_values("frames.predictions", predictions)

print(dataset)

# Evaluate the frame-level `predictions` against the frame-level
# `detections` objects
results = dataset.evaluate_detections(
    "frames.predictions",
    gt_field="frames.detections",
    eval_key="eval",
)

# Print a classification report
results.print_report()

              precision    recall  f1-score   support

      person       0.76      0.93      0.84      1108
   road sign       0.90      0.94      0.92      2726
     vehicle       0.98      0.94      0.96      7511

   micro avg       0.94      0.94      0.94     11345
   macro avg       0.88      0.94      0.91     11345
weighted avg       0.94      0.94      0.94     11345

You can also view frame-level evaluation results as evaluation patches by first converting to frames and then to patches!

# Convert to frame evaluation patches
frames = dataset.to_frames(sample_frames=True)
frame_eval_patches = frames.to_evaluation_patches("eval")
print(frame_eval_patches)

print(frame_eval_patches.count_values("type"))
# {'tp': 10578, 'fn': 767, 'fp': 767}

session = fo.launch_app(view=frame_eval_patches)

Dataset:     video-eval-demo
Media type:  image
Num patches: 12112
Patch fields:
    id:               fiftyone.core.fields.ObjectIdField
    sample_id:        fiftyone.core.fields.ObjectIdField
    frame_id:         fiftyone.core.fields.ObjectIdField
    filepath:         fiftyone.core.fields.StringField
    frame_number:     fiftyone.core.fields.FrameNumberField
    tags:             fiftyone.core.fields.ListField(fiftyone.core.fields.StringField)
    metadata:         fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.metadata.ImageMetadata)
    created_at:       fiftyone.core.fields.DateTimeField
    last_modified_at: fiftyone.core.fields.DateTimeField
    predictions:      fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)
    detections:       fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)
    type:             fiftyone.core.fields.StringField
    iou:              fiftyone.core.fields.FloatField
    crowd:            fiftyone.core.fields.BooleanField
View stages:
    1. ToFrames(config=None)
    2. ToEvaluationPatches(eval_key='eval', config=None)

Custom evaluation backends

If you would like to use an evaluation protocol that is not natively supported by FiftyOne, you can follow the instructions below to implement an interface for your protocol and then configure your environment so that FiftyOne’s evaluation methods will use it.

Regression

Classification

Detection

Segmentation

You can define custom regression evaluation backends that can be used by passing the method parameter to evaluate_regressions():

view.evaluate_regressions(..., method="<backend>", ...)

Regression evaluation backends are defined by writing subclasses of the following two classes:

• RegressionEvaluation: this class implements the evaluation protocol itself. Specifically you should implement evaluate_samples(), which accepts a sample collection to evaluate as input and returns a RegressionResults instance that contains the results of the evaluation

• RegressionEvaluationConfig: this class defines the available parameters that users can pass as keyword arguments to evaluate_regressions() to customize the behavior of the evaluation run

If desired, you can also implement and return a custom RegressionResults subclass. This is useful if you want to expose custom methods that users can call to view and/or interact with the evaluation results programmatically.

The recommended way to expose a custom regression evaluation method is to add it to your evaluation config at ~/.fiftyone/evaluation_config.json as follows:

{
    "default_regression_backend": "<backend>",
    "regression_backends": {
        "<backend>": {
            "config_cls": "your.custom.RegressionEvaluationConfig"
        }
    },
    ...
}

In the above, <backend> defines the name of your custom backend, which you can henceforward pass as the method parameter to evaluate_regressions(), and the config_cls parameter specifies the fully-qualified name of the RegressionEvaluationConfig subclass for your evaluation backend.

With the optional default_regression_backend parameter set to your custom backend as shown above, calling evaluate_regressions() will automatically use your backend.

You can define custom classification evaluation backends that can be used by passing the method parameter to evaluate_classifications():

view.evaluate_classifications(..., method="<backend>", ...)

Classification evaluation backends are defined by writing subclasses of the following two classes:

• ClassificationEvaluation: this class implements the evaluation protocol itself. Specifically you should implement evaluate_samples(), which accepts a sample collection to evaluate as input and returns a ClassificationResults instance that contains the results of the evaluation

• ClassificationEvaluationConfig: this class defines the available parameters that users can pass as keyword arguments to evaluate_classifications() to customize the behavior of the evaluation run

If desired, you can also implement and return a custom ClassificationResults subclass. This is useful if you want to expose custom methods that users can call to view and/or interact with the evaluation results programmatically.

The recommended way to expose a custom classification evaluation method is to add it to your evaluation config at ~/.fiftyone/evaluation_config.json as follows:

{
    "default_classification_backend": "<backend>",
    "classification_backends": {
        "<backend>": {
            "config_cls": "your.custom.ClassificationEvaluationConfig"
        }
    },
    ...
}

In the above, <backend> defines the name of your custom backend, which you can henceforward pass as the method parameter to evaluate_classifications(), and the config_cls parameter specifies the fully-qualified name of the ClassificationEvaluationConfig subclass for your evaluation backend.

With the optional default_classification_backend parameter set to your custom backend as shown above, calling evaluate_classifications() will automatically use your backend.

You can define custom detection evaluation backends that can be used by passing the method parameter to evaluate_detections():

view.evaluate_detections(..., method="<backend>", ...)

Detection evaluation backends are defined by writing subclasses of the following two classes:

• DetectionEvaluation: this class implements the evaluation protocol itself. Specifically you should implement evaluate(), which accepts a sample to evaluate as input and returns a list of matched ground truth/predicted object pairs, and you can optionally implement generate_results(), to compute aggregate evaluation results (e.g., mAP or PR curves) for the sample collection and return them in a DetectionResults instance

• DetectionEvaluationConfig: this class defines the available parameters that users can pass as keyword arguments to evaluate_detections() to customize the behavior of the evaluation run

If desired, you can also implement and return a custom DetectionResults subclass. This is useful if you want to expose custom methods that users can call to view and/or interact with the evaluation results programmatically.

The recommended way to expose a custom detection evaluation method is to add it to your evaluation config at ~/.fiftyone/evaluation_config.json as follows:

{
    "default_detection_backend": "<backend>",
    "detection_backends": {
        "<backend>": {
            "config_cls": "your.custom.DetectionEvaluationConfig"
        }
    },
    ...
}

In the above, <backend> defines the name of your custom backend, which you can henceforward pass as the method parameter to evaluate_detections(), and the config_cls parameter specifies the fully-qualified name of the DetectionEvaluationConfig subclass for your evaluation backend.

With the optional default_detection_backend parameter set to your custom backend as shown above, calling evaluate_detections() will automatically use your backend.

You can define custom segmentation evaluation backends that can be used by passing the method parameter to evaluate_segmentations():

view.evaluate_segmentations(..., method="<backend>", ...)

Segmentation evaluation backends are defined by writing subclasses of the following two classes:

• SegmentationEvaluation: this class implements the evaluation protocol itself. Specifically you should implement evaluate_samples(), which accepts a sample collection to evaluate as input and returns a SegmentationResults instance that contains the results of the evaluation

• SegmentationEvaluationConfig: this class defines the available parameters that users can pass as keyword arguments to evaluate_segmentations() to customize the behavior of the evaluation run

If desired, you can also implement and return a custom SegmentationResults subclass. This is useful if you want to expose custom methods that users can call to view and/or interact with the evaluation results programmatically.

The recommended way to expose a custom segmentation evaluation method is to add it to your evaluation config at ~/.fiftyone/evaluation_config.json as follows:

{
    "default_segmentation_backend": "<backend>",
    "segmentation_backends": {
        "<backend>": {
            "config_cls": "your.custom.SegmentationEvaluationConfig"
        }
    },
    ...
}

In the above, <backend> defines the name of your custom backend, which you can henceforward pass as the method parameter to evaluate_segmentations(), and the config_cls parameter specifies the fully-qualified name of the SegmentationEvaluationConfig subclass for your evaluation backend.

With the optional default_segmentation_backend parameter set to your custom backend as shown above, calling evaluate_segmentations() will automatically use your backend.

Evaluation config

FiftyOne provides an evaluation config that you can use to either temporarily or permanently configure the behavior of the evaluation API.

Viewing your config

You can print your current evaluation config at any time via the Python library and the CLI:

Python

CLI

import fiftyone as fo

# Print your current evaluation config
print(fo.evaluation_config)

{
    "default_regresion_backend": "simple",
    "default_classification_backend": "simple",
    "default_detection_backend": "coco",
    "default_segmentation_backend": "simple",
    "regression_backends": {
        "simple": {
            "config_cls": "fiftyone.utils.eval.regression.SimpleEvaluationConfig"
        }
    },
    "classification_backends": {
        "binary": {
            "config_cls": "fiftyone.utils.eval.classification.BinaryEvaluationConfig"
        },
        "simple": {
            "config_cls": "fiftyone.utils.eval.classification.SimpleEvaluationConfig"
        },
        "top-k": {
            "config_cls": "fiftyone.utils.eval.classification.TopKEvaluationConfig"
        }
    },
    "detection_backends": {
        "activitynet": {
            "config_cls": "fiftyone.utils.eval.activitynet.ActivityNetEvaluationConfig"
        },
        "coco": {
            "config_cls": "fiftyone.utils.eval.coco.COCOEvaluationConfig"
        },
        "open-images": {
            "config_cls": "fiftyone.utils.eval.openimages.OpenImagesEvaluationConfig"
        }
    },
    "segmentation_backends": {
        "simple": {
            "config_cls": "fiftyone.utils.eval.segmentation.SimpleEvaluationConfig"
        }
    }
}

# Print your current evaluation config
fiftyone evaluation config

{
    "default_regresion_backend": "simple",
    "default_classification_backend": "simple",
    "default_detection_backend": "coco",
    "default_segmentation_backend": "simple",
    "regression_backends": {
        "simple": {
            "config_cls": "fiftyone.utils.eval.regression.SimpleEvaluationConfig"
        }
    },
    "classification_backends": {
        "binary": {
            "config_cls": "fiftyone.utils.eval.classification.BinaryEvaluationConfig"
        },
        "simple": {
            "config_cls": "fiftyone.utils.eval.classification.SimpleEvaluationConfig"
        },
        "top-k": {
            "config_cls": "fiftyone.utils.eval.classification.TopKEvaluationConfig"
        }
    },
    "detection_backends": {
        "activitynet": {
            "config_cls": "fiftyone.utils.eval.activitynet.ActivityNetEvaluationConfig"
        },
        "coco": {
            "config_cls": "fiftyone.utils.eval.coco.COCOEvaluationConfig"
        },
        "open-images": {
            "config_cls": "fiftyone.utils.eval.openimages.OpenImagesEvaluationConfig"
        }
    },
    "segmentation_backends": {
        "simple": {
            "config_cls": "fiftyone.utils.eval.segmentation.SimpleEvaluationConfig"
        }
    }
}

Note

If you have customized your evaluation config via any of the methods described below, printing your config is a convenient way to ensure that the changes you made have taken effect as you expected.

Modifying your config

You can modify your evaluation config in a variety of ways. The following sections describe these options in detail.

Order of precedence

The following order of precedence is used to assign values to your evaluation config settings as runtime:

1. Config settings applied at runtime by directly editing fiftyone.evaluation_config

2. FIFTYONE_XXX environment variables

3. Settings in your JSON config (~/.fiftyone/evaluation_config.json)

4. The default config values

Editing your JSON config

You can permanently customize your evaluation config by creating a ~/.fiftyone/evaluation_config.json file on your machine. The JSON file may contain any desired subset of config fields that you wish to customize.

For example, the following config JSON file declares a new custom detection evaluation backend without changing any other default config settings:

{
    "default_detection_backend": "custom",
    "detection_backends": {
        "custom": {
            "config_cls": "path.to.your.CustomDetectionEvaluationConfig"
        }
    }
}

When fiftyone is imported, any options from your JSON config are merged into the default config, as per the order of precedence described above.

Note

You can customize the location from which your JSON config is read by setting the FIFTYONE_EVALUATION_CONFIG_PATH environment variable.

Setting environment variables

Evaluation config settings may be customized on a per-session basis by setting the FIFTYONE_<TYPE>_XXX environment variable(s) for the desired config settings, where <TYPE> can be REGRESSION, CLASSIFICATION, DETECTION, or SEGMENTATION.

The FIFTYONE_DEFAULT_<TYPE>_BACKEND environment variables allows you to configure your default backend:

export FIFTYONE_DEFAULT_DETECTION_BACKEND=coco

You can declare parameters for specific evaluation backends by setting environment variables of the form FIFTYONE_<TYPE>_<BACKEND>_<PARAMETER>. Any settings that you declare in this way will be passed as keyword arguments to methods like evaluate_detections() whenever the corresponding backend is in use:

export FIFTYONE_DETECTION_COCO_ISCROWD=is_crowd

The FIFTYONE_<TYPE>_BACKENDS environment variables can be set to a list,of,backends that you want to expose in your session, which may exclude native backends and/or declare additional custom backends whose parameters are defined via additional config modifications of any kind:

export FIFTYONE_DETECTION_BACKENDS=custom,coco,open-images

When declaring new backends, you can include * to append new backend(s) without omitting or explicitly enumerating the builtin backends. For example, you can add a custom detection evaluation backend as follows:

export FIFTYONE_DETECTION_BACKENDS=*,custom
export FIFTYONE_DETECTION_CUSTOM_CONFIG_CLS=your.custom.DetectionEvaluationConfig

Modifying your config in code

You can dynamically modify your evaluation config at runtime by directly editing the fiftyone.evaluation_config object.

Any changes to your evaluation config applied via this manner will immediately take effect in all subsequent calls to fiftyone.evaluation_config during your current session.

import fiftyone as fo

fo.evaluation_config.default_detection_backend = "custom"