How do I install the Fiftyone Model Evaluation skill?

Run npx skillmds@latest add voxel51/fiftyone-model-evaluation in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

What does the Fiftyone Model Evaluation skill do?

Evaluate model predictions against ground truth using COCO, Open Images, or custom protocols. Use when computing mAP, precision, recall, confusion matrices, or analyzing TP/FP/FN examples for detection, classification, segmentation, or regression tasks. It is listed under AI & ML on SkillMD.

Is Fiftyone Model Evaluation safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Fiftyone Model Evaluation?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Fiftyone Model Evaluation free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Fiftyone Model Evaluation?

voxel51 (@voxel51) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Fiftyone Model Evaluation by voxel51

Name: Fiftyone Model Evaluation
Author: voxel51

Evaluate Model Predictions in FiftyOne

Key Directives

ALWAYS follow these rules:

1. Check if dataset exists and has required fields

list_datasets()
set_context(dataset_name="my-dataset")
dataset_summary(name="my-dataset")

Verify the dataset has both prediction and ground truth fields of compatible types.

2. Install evaluation plugin if not available

list_plugins()
# If @voxel51/evaluation not listed:
download_plugin(url_or_repo="voxel51/fiftyone-plugins", plugin_names=["@voxel51/evaluation"])
enable_plugin(plugin_name="@voxel51/evaluation")

3. Ask user for evaluation parameters

Always confirm with the user:

Prediction field name
Ground truth field name
Evaluation key (unique identifier for this evaluation)
Evaluation method (coco, open-images, simple, top-k, binary)
Whether to compute mAP (for detection tasks)

4. Launch App for evaluation operators

launch_app(dataset_name="my-dataset")

5. Close app when done

close_app()

Workflow

Step 1: Verify Dataset and Fields

list_datasets()
set_context(dataset_name="my-dataset")
dataset_summary(name="my-dataset")

Review:

Sample count
Available label fields and their types
Identify prediction field (model outputs)
Identify ground truth field (annotations)

Label Types and Compatible Evaluations:

Label Type	Evaluation Method	Supported Methods
`Detections`	`evaluate_detections()`	coco, open-images
`Polylines`	`evaluate_detections()`	coco, open-images
`Keypoints`	`evaluate_detections()`	coco, open-images
`TemporalDetections`	`evaluate_detections()`	activitynet
`Classification`	`evaluate_classifications()`	simple, top-k, binary
`Segmentation`	`evaluate_segmentations()`	simple
`Regression`	`evaluate_regressions()`	simple

Step 2: Ensure Evaluation Plugin is Installed

list_plugins()

If @voxel51/evaluation is not in the list:

download_plugin(
    url_or_repo="voxel51/fiftyone-plugins",
    plugin_names=["@voxel51/evaluation"]
)
enable_plugin(plugin_name="@voxel51/evaluation")

Step 3: Launch App

launch_app(dataset_name="my-dataset")

Step 4: Run Evaluation

Ask user for:

Prediction field (pred_field)
Ground truth field (gt_field)
Evaluation key (eval_key) - must be unique identifier
Evaluation method

execute_operator(
    operator_uri="@voxel51/evaluation/evaluate_model",
    params={
        "pred_field": "predictions",
        "gt_field": "ground_truth",
        "eval_key": "eval",
        "method": "coco",
        "iou": 0.5,
        "compute_mAP": true
    }
)

Step 5: View Results

After evaluation, the dataset will have new fields:

{eval_key}_tp - True positive count per sample
{eval_key}_fp - False positive count per sample
{eval_key}_fn - False negative count per sample

View only samples with false positives:

set_view(filters={"eval_fp": {"$gt": 0}})

Use the Model Evaluation Panel in the App to interactively explore:

Summary metrics (mAP, precision, recall)
Confusion matrices
Per-class performance
Scenario analysis

Step 6: View Evaluation Patches (TP/FP/FN)

To examine individual true positives, false positives, and false negatives, guide users to the Python SDK:

import fiftyone as fo

dataset = fo.load_dataset("my-dataset")

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

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

# View only false positives
fp_view = eval_patches.match(F("type") == "fp")
session = fo.launch_app(view=fp_view)

Step 7: Clean Up

close_app()

Evaluation Types

Detection Evaluation

For Detections, Polylines, Keypoints labels.

COCO-style (default):

execute_operator(
    operator_uri="@voxel51/evaluation/evaluate_model",
    params={
        "pred_field": "predictions",
        "gt_field": "ground_truth",
        "eval_key": "eval_coco",
        "method": "coco",
        "iou": 0.5,
        "classwise": true,
        "compute_mAP": true
    }
)

Parameter	Type	Default	Description
`iou`	float	0.5	IoU threshold for matching
`classwise`	bool	true	Only match objects with same class
`compute_mAP`	bool	false	Compute mAP, mAR, and PR curves
`use_masks`	bool	false	Use instance masks for IoU (if available)
`iscrowd`	string	null	Attribute name for crowd annotations
`iou_threshs`	string	null	Comma-separated IoU thresholds for mAP
`max_preds`	int	null	Max predictions per sample for mAP

Open Images-style:

execute_operator(
    operator_uri="@voxel51/evaluation/evaluate_model",
    params={
        "pred_field": "predictions",
        "gt_field": "ground_truth",
        "eval_key": "eval_oi",
        "method": "open-images",
        "iou": 0.5
    }
)

Supports additional parameters:

pos_label_field: Classifications specifying which classes should be evaluated
neg_label_field: Classifications specifying which classes should NOT be evaluated

ActivityNet-style (temporal):

For TemporalDetections in video datasets:

execute_operator(
    operator_uri="@voxel51/evaluation/evaluate_model",
    params={
        "pred_field": "predictions",
        "gt_field": "ground_truth",
        "eval_key": "eval_temporal",
        "method": "activitynet",
        "compute_mAP": true
    }
)

Classification Evaluation

For Classification labels.

Simple (default):

execute_operator(
    operator_uri="@voxel51/evaluation/evaluate_model",
    params={
        "pred_field": "predictions",
        "gt_field": "ground_truth",
        "eval_key": "eval_cls",
        "method": "simple"
    }
)

Per-sample field {eval_key} stores boolean indicating if prediction was correct.

Top-k:

Requires predictions with logits field:

execute_operator(
    operator_uri="@voxel51/evaluation/evaluate_model",
    params={
        "pred_field": "predictions",
        "gt_field": "ground_truth",
        "eval_key": "eval_topk",
        "method": "top-k",
        "k": 5
    }
)

Binary:

For binary classifiers:

execute_operator(
    operator_uri="@voxel51/evaluation/evaluate_model",
    params={
        "pred_field": "predictions",
        "gt_field": "ground_truth",
        "eval_key": "eval_binary",
        "method": "binary"
    }
)

Per-sample field {eval_key} stores: "tp", "fp", "tn", or "fn".

Segmentation Evaluation

For Segmentation labels.

execute_operator(
    operator_uri="@voxel51/evaluation/evaluate_model",
    params={
        "pred_field": "predictions",
        "gt_field": "ground_truth",
        "eval_key": "eval_seg",
        "method": "simple",
        "bandwidth": 5  # Optional: evaluate only boundary pixels
    }
)

Parameter	Type	Default	Description
`bandwidth`	int	null	Pixels along contours to evaluate (null = entire mask)
`average`	string	"micro"	Averaging strategy: micro, macro, weighted, samples

Per-sample fields:

{eval_key}_accuracy
{eval_key}_precision
{eval_key}_recall

Regression Evaluation

For Regression labels.

execute_operator(
    operator_uri="@voxel51/evaluation/evaluate_model",
    params={
        "pred_field": "predictions",
        "gt_field": "ground_truth",
        "eval_key": "eval_reg",
        "method": "simple",
        "metric": "squared_error"  # or "absolute_error"
    }
)

Per-sample field {eval_key} stores the error value.

Metrics available:

Mean Squared Error (MSE)
Root Mean Squared Error (RMSE)
Mean Absolute Error (MAE)
Median Absolute Error
R² Score
Explained Variance Score
Max Error

Managing Evaluations

List Existing Evaluations

execute_operator(
    operator_uri="@voxel51/evaluation/get_evaluation_info",
    params={
        "eval_key": "eval"
    }
)

Load Evaluation View

Load the exact view on which an evaluation was performed:

execute_operator(
    operator_uri="@voxel51/evaluation/load_evaluation_view",
    params={
        "eval_key": "eval",
        "select_fields": false
    }
)

Rename Evaluation

execute_operator(
    operator_uri="@voxel51/evaluation/rename_evaluation",
    params={
        "eval_key": "eval",
        "new_eval_key": "eval_v2"
    }
)

Delete Evaluation

execute_operator(
    operator_uri="@voxel51/evaluation/delete_evaluation",
    params={
        "eval_key": "eval"
    }
)

Common Use Cases

Use Case 1: Evaluate Object Detection Model

# Verify dataset has detection fields
set_context(dataset_name="my-dataset")
dataset_summary(name="my-dataset")

# Launch app
launch_app(dataset_name="my-dataset")

# Run COCO-style evaluation with mAP
execute_operator(
    operator_uri="@voxel51/evaluation/evaluate_model",
    params={
        "pred_field": "predictions",
        "gt_field": "ground_truth",
        "eval_key": "eval",
        "method": "coco",
        "iou": 0.5,
        "compute_mAP": true
    }
)

# View samples with most false positives
set_view(filters={"eval_fp": {"$gt": 5}})

Use Case 2: Compare Two Detection Models

set_context(dataset_name="my-dataset")
launch_app(dataset_name="my-dataset")

# Evaluate first model
execute_operator(
    operator_uri="@voxel51/evaluation/evaluate_model",
    params={
        "pred_field": "model_a_predictions",
        "gt_field": "ground_truth",
        "eval_key": "eval_model_a",
        "method": "coco",
        "compute_mAP": true
    }
)

# Evaluate second model
execute_operator(
    operator_uri="@voxel51/evaluation/evaluate_model",
    params={
        "pred_field": "model_b_predictions",
        "gt_field": "ground_truth",
        "eval_key": "eval_model_b",
        "method": "coco",
        "compute_mAP": true
    }
)

# Use the Model Evaluation Panel to compare results

Use Case 3: Evaluate Classification Model

set_context(dataset_name="my-classification-dataset")
launch_app(dataset_name="my-classification-dataset")

# Simple classification evaluation
execute_operator(
    operator_uri="@voxel51/evaluation/evaluate_model",
    params={
        "pred_field": "predictions",
        "gt_field": "ground_truth",
        "eval_key": "eval_cls",
        "method": "simple"
    }
)

# View misclassified samples
set_view(filters={"eval_cls": false})

Use Case 4: Evaluate at Different IoU Thresholds

set_context(dataset_name="my-dataset")
launch_app(dataset_name="my-dataset")

# Strict evaluation (IoU 0.75)
execute_operator(
    operator_uri="@voxel51/evaluation/evaluate_model",
    params={
        "pred_field": "predictions",
        "gt_field": "ground_truth",
        "eval_key": "eval_strict",
        "method": "coco",
        "iou": 0.75,
        "compute_mAP": true
    }
)

# Lenient evaluation (IoU 0.25)
execute_operator(
    operator_uri="@voxel51/evaluation/evaluate_model",
    params={
        "pred_field": "predictions",
        "gt_field": "ground_truth",
        "eval_key": "eval_lenient",
        "method": "coco",
        "iou": 0.25,
        "compute_mAP": true
    }
)

Use Case 5: Evaluate Segmentation Model

set_context(dataset_name="my-segmentation-dataset")
launch_app(dataset_name="my-segmentation-dataset")

# Full mask evaluation
execute_operator(
    operator_uri="@voxel51/evaluation/evaluate_model",
    params={
        "pred_field": "predictions",
        "gt_field": "ground_truth",
        "eval_key": "eval_seg",
        "method": "simple"
    }
)

# Boundary-only evaluation (5 pixel bandwidth)
execute_operator(
    operator_uri="@voxel51/evaluation/evaluate_model",
    params={
        "pred_field": "predictions",
        "gt_field": "ground_truth",
        "eval_key": "eval_seg_boundary",
        "method": "simple",
        "bandwidth": 5
    }
)

Python SDK Alternative

For more control over evaluation and access to full results, guide users to the Python SDK:

import fiftyone as fo
import fiftyone.zoo as foz

# Load dataset
dataset = fo.load_dataset("my-dataset")

# Evaluate detections
results = dataset.evaluate_detections(
    "predictions",
    gt_field="ground_truth",
    eval_key="eval",
    method="coco",
    iou=0.5,
    compute_mAP=True,
)

# Print classification report
results.print_report()

# Get mAP value
print(f"mAP: {results.mAP():.3f}")

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

# Plot precision-recall curves
plot = results.plot_pr_curves(classes=["person", "car", "dog"])
plot.show()

# Convert to evaluation patches to view TP/FP/FN
eval_patches = dataset.to_evaluation_patches("eval")
print(eval_patches.count_values("type"))

# View false positives in the App
from fiftyone import ViewField as F
fp_view = eval_patches.match(F("type") == "fp")
session = fo.launch_app(view=fp_view)

Python SDK evaluation methods:

dataset.evaluate_detections() - Object detection
dataset.evaluate_classifications() - Classification
dataset.evaluate_segmentations() - Semantic segmentation
dataset.evaluate_regressions() - Regression

Results object methods:

results.print_report() - Print classification report
results.print_metrics() - Print aggregate metrics
results.mAP() - Get mAP value (detection only)
results.mAR() - Get mAR value (detection only)
results.plot_confusion_matrix() - Interactive confusion matrix
results.plot_pr_curves() - Precision-recall curves
results.plot_results() - Scatter plot (regression only)

Troubleshooting

Error: "No suitable label fields"

Dataset must have label fields of compatible types
Use dataset_summary() to see available fields and types

Error: "No suitable ground truth fields"

Ground truth field must be same type as prediction field
Cannot compare Detections predictions with Classification ground truth

Error: "Evaluation key already exists"

Each evaluation must have a unique key
Delete existing evaluation or use a different key name

Error: "Plugin not found"

Install the evaluation plugin:

download_plugin(url_or_repo="voxel51/fiftyone-plugins", plugin_names=["@voxel51/evaluation"])
enable_plugin(plugin_name="@voxel51/evaluation")

mAP is not computed

Set compute_mAP: true in params
mAP requires multiple predictions per image to be meaningful

Evaluation is slow

Large datasets take time
Consider evaluating a filtered view first
Use delegated execution for background processing

Best Practices

Use descriptive eval_keys - eval_yolov8_coco, eval_resnet_topk5
Don't overwrite evaluations - Use unique keys for each evaluation run
Compare at same IoU - When comparing models, use consistent IoU thresholds
Check field types first - Ensure prediction and ground truth fields are compatible
Use Model Evaluation Panel - Interactive exploration is easier than scripting
Examine patches - Use to_evaluation_patches() to understand errors

Fiftyone Model Evaluation

File contents

Evaluate Model Predictions in FiftyOne

Key Directives

1. Check if dataset exists and has required fields

2. Install evaluation plugin if not available

3. Ask user for evaluation parameters

4. Launch App for evaluation operators

5. Close app when done

Workflow

Step 1: Verify Dataset and Fields

Step 2: Ensure Evaluation Plugin is Installed

Step 3: Launch App

Step 4: Run Evaluation

Step 5: View Results

Step 6: View Evaluation Patches (TP/FP/FN)

Step 7: Clean Up

Evaluation Types

Detection Evaluation

Classification Evaluation

Segmentation Evaluation

Regression Evaluation

Managing Evaluations

List Existing Evaluations

Load Evaluation View

Rename Evaluation

Delete Evaluation

Common Use Cases

Use Case 1: Evaluate Object Detection Model

Use Case 2: Compare Two Detection Models

Use Case 3: Evaluate Classification Model

Use Case 4: Evaluate at Different IoU Thresholds

Use Case 5: Evaluate Segmentation Model

Python SDK Alternative

Troubleshooting

Best Practices

Resources

Frequently asked questions

Fiftyone Model Evaluation

File contents

Evaluate Model Predictions in FiftyOne

Key Directives

1. Check if dataset exists and has required fields

2. Install evaluation plugin if not available

3. Ask user for evaluation parameters

4. Launch App for evaluation operators

5. Close app when done

Workflow

Step 1: Verify Dataset and Fields

Step 2: Ensure Evaluation Plugin is Installed

Step 3: Launch App

Step 4: Run Evaluation

Step 5: View Results

Step 6: View Evaluation Patches (TP/FP/FN)

Step 7: Clean Up

Evaluation Types

Detection Evaluation

Classification Evaluation

Segmentation Evaluation

Regression Evaluation

Managing Evaluations

List Existing Evaluations

Load Evaluation View

Rename Evaluation

Delete Evaluation

Common Use Cases

Use Case 1: Evaluate Object Detection Model

Use Case 2: Compare Two Detection Models

Use Case 3: Evaluate Classification Model

Use Case 4: Evaluate at Different IoU Thresholds

Use Case 5: Evaluate Segmentation Model

Python SDK Alternative

Troubleshooting

Best Practices

Resources

Related skills

Frequently asked questions

How do I install the Fiftyone Model Evaluation skill?

What does the Fiftyone Model Evaluation skill do?

Is Fiftyone Model Evaluation safe to use?

Which AI agents work with Fiftyone Model Evaluation?

Is Fiftyone Model Evaluation free to use?

Who published Fiftyone Model Evaluation?