AutoGineer
Generated banner for Tao Analyze Detection Kpi
Generated logo for Tao Analyze Detection Kpi

Tao Analyze Detection Kpi

NVIDIAOfficialCreated by NVIDIA Corporation

  • Skill
  • Review and audit
  • Software development
  • Apache-2.0
  • Medium risk
  • Has scripts
Download ZIP

From the author

Run TAO Data Services KPI analysis for object detection, comparing inference annotations against ground truth to compute per-class TP/FP/FN/TN, precision, recall, accuracy, and AP at a fixed IoU. Use when an object detection workflow needs per-class mAP reported after inference, or when the user asks to "run KPI analyze", "compute detection mAP", or "score my OD predictions against ground truth".
— the description by NVIDIA Corporation

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a structured way to evaluate object detection models using TAO Data Services. It compares inference annotations against ground truth to calculate key performance indicators including True Positives, False Positives, False Negatives, precision, recall, accuracy, and Average Precision (AP) at a fixed IoU. The process generates a per-class metrics CSV and optional PR curve plots, enabling developers to quantify model performance across different datasets.

What it does

  • Calculates per-class detection metrics like TP, FP, FN, and AP.
  • Supports both KITTI and COCO annotation formats.
  • Generates a comprehensive per-class metrics CSV report.
  • Produces PR curve visualizations for performance analysis.
  • Allows for custom class mapping to aggregate multiple labels into canonical classes.
  • Enables threshold-based filtering for precision-recall evaluation.

When to use it

  • When you need to validate object detection model performance after inference.
  • When comparing model predictions against ground truth datasets.
  • When you need to generate standardized mAP reports for model evaluation.
  • When you need to sweep confidence thresholds to analyze the precision-recall curve.

Advantages

  • Provides granular per-class performance insights.
  • Supports flexible class mapping to handle diverse label naming conventions.
  • Enables reproducible evaluation by keeping spec files alongside results.
  • Integrates directly with TAO Data Services for consistent metric calculation.

Usage examples

“Compute the detection mAP for my latest object detection model run.”
The agent will guide you to configure the kpi_analyze.yaml spec with your inference and ground truth paths, then execute the analysis to produce a kpi_calc.csv report.
“Score my OD predictions against ground truth using a 0.5 IoU threshold.”
The agent will verify your input paths and mapping file, then run the KPI analysis container to output the precision, recall, and AP metrics.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/nvidia/tao-analyze-detection-kpi/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip tao-analyze-detection-kpi.zip -d .agents/skills

Documentation by NVIDIA Corporation

TAO Analyze Detection KPI

Use this skill to run TAO Data Services KPI analysis for object detection. The skill compares inference annotations against ground truth over one or more KPI sources and writes a per-class metrics CSV. It does not run inference; an upstream step must produce the inference annotations first.

The container entrypoint is:

analytics kpi_analyze -e /absolute/path/to/kpi_analyze.yaml

Inputs

The user provides either a finished spec or the paths to fill into the template.

Required spec fields:

FieldMeaning
data.input_formatKITTI or COCO. Uppercase — see Pitfalls.
data.kpi_sourcesList of sources. Each entry requires image_dir, ground_truth_ann_path, and inference_ann_path; all three are asserted at startup.
data.mappingPath to a class-mapping YAML: a list of single-key dicts whose value is a LIST of aliases. See assets/example_mapping.yaml — a bare string here silently zeroes every metric.
results_dirOutput directory for kpi_calc.csv.

Common optional fields. The Default column is what TAO DS uses when the field is absent; assets/default_kpi_analyze.yaml already carries the recommended value for each, so filling the template needs none of them changed:

FieldDefaultMeaning
kpi.iou_threshold0.5IoU at or above which a prediction counts as a true positive.
kpi.conf_threshold0.5Predictions below this are dropped. The template uses 0.0, which keeps the whole PR curve so a threshold can be swept afterwards without re-running inference. On the pinned image that is safe: unmatched ground truth carries a -1.0 sentinel and lands in FN at any threshold. On a build predating that fix, 0.0 scored every missed box as a true positive — TP became the ground-truth count and FN was always 0 — so use a small positive value there.
kpi.num_recall_points11Recall points for the interpolated PR curve. The template keeps 101 (COCO-style), matching the reference ITS pipeline. 11 selects VOC-style sampling and reports different numbers for the same detections.
kpi.ignore_sqwidth0Boxes narrower than this are ignored. The template keeps 0, matching the reference ITS pipeline, which scores every box. A higher value raises AP by dropping the hardest detections rather than by detecting them.
kpi.filterfalseEnable source filtering.
kpi.is_internalfalseWhen true, drops every class except person and appends a Summary row.
visualize.platformlocallocal writes a PR-curve plot into results_dir; wandb logs a run and table instead.
visualize.tagnullTag recorded on every row.

For a Grounding DINO loop, inference_ann_path is the labels/ directory TAO inference writes under {results_dir}/inference/labels/, and input_format is KITTI.

The default template is assets/default_kpi_analyze.yaml.

Quick Start

Run from the tao-skill-bank repo root.

Write the spec into the results directory. The run does not retain it, so a completed run otherwise cannot tell you which settings produced kpi_calc.csv. Keeping them together makes the result reproducible from the run alone.

RESULTS_DIR=/absolute/path/for/this/run          # results_dir in the spec
SPEC="$RESULTS_DIR/kpi_analyze.yaml"        # spec lives beside its outputs
RUN_ROOT=/absolute/path/that/contains/images/annotations/and/results

python3 skills/data/tao-analyze-detection-kpi/scripts/verify_kpi_analyze_spec.py \
  --spec "$SPEC"

DS_IMAGE="$(scripts/resolve_versions_key.py images.tao_toolkit.data_services)"

docker run --rm --gpus all --shm-size=8g --network=host \
  -v "$RUN_ROOT:$RUN_ROOT" \
  -w "$RUN_ROOT" \
  "$DS_IMAGE" \
  analytics kpi_analyze -e "$SPEC"

Pass --gpus all even though the analysis itself is CPU-only. The TAO launcher calls nvidia-smi -L unconditionally before dispatching any subtask, so a container started without GPU access dies with FileNotFoundError: 'nvidia-smi' before kpi_analyze runs.

Do not pass --user $(id -u):$(id -g); some TAO DS images call getpass.getuser() at startup and fail when the UID is not in /etc/passwd.

Generate A Spec

If the user provides paths instead of a ready spec, copy the template and fill in the nulls. Every tuning value it already carries is the one this stage wants — change one only deliberately.

cp skills/data/tao-analyze-detection-kpi/assets/default_kpi_analyze.yaml "$SPEC"

Fill data.kpi_sources (one entry per source), data.mapping and results_dir, all as absolute paths, then validate:

python3 skills/data/tao-analyze-detection-kpi/scripts/verify_kpi_analyze_spec.py --spec "$SPEC"
data:
  input_format: KITTI
  kpi_sources:
  - image_dir: /absolute/path/kpi/images               # no trailing slash
    ground_truth_ann_path: /absolute/path/kpi/labels
    inference_ann_path: /absolute/path/results/inference/labels
  mapping: /absolute/path/mapping.yaml
results_dir: /absolute/path/results/analyze_kpi

The template is the only place a default value lives, so nothing can disagree with it. verify reports the three settings that change what the numbers mean — conf_threshold, num_recall_points, ignore_sqwidth — so the spec that ran is recoverable from its output.

Preflight

  1. Verify Docker access:

docker info > /dev/null

  1. Resolve and pull the data-services image if needed:

DS_IMAGE="$(scripts/resolve_versions_key.py images.tao_toolkit.data_services)"
docker image inspect "$DS_IMAGE" > /dev/null || docker pull "$DS_IMAGE"

  1. Validate the spec:

python3 skills/data/tao-analyze-detection-kpi/scripts/verify_kpi_analyze_spec.py --spec "$SPEC"

  1. Confirm RUN_ROOT contains the spec, every image_dir, both annotation paths per source, the mapping file, and the results directory. Mount RUN_ROOT to the same absolute path inside Docker.

Outputs

ArtifactLocation
Per-class metrics CSVresults_dir/kpi_calc.csv
PR curve plotresults_dir/ (only when visualize.platform: local)

kpi_calc.csv columns: Sequence Name, TP, FP, FN, TN, Pr, Re, Acc, AP — one row per sequence per class. A per-class result table and the aggregate mAP are also printed to stdout; capture the log if the caller needs the mAP value, since it is not written to the CSV.

Pitfalls

Ground truth may be 15- or 16-field KITTI. The parser names 15 columns for ground truth and 16 for predictions, but reads with index_col=False, so a trailing conf_score on a GT file is truncated rather than shifted. Feeding GT straight from tooling that writes a score column is fine — verified byte-identical results either way. The ParserWarning about "length of header or names does not match length of data" describes exactly that truncation and is not a sign of corruption.

input_format is uppercase here. analytics kpi_analyze accepts only KITTI or COCO. This differs from gap_analysis object_detection, which takes lowercase kitti / coco. Passing lowercase to this action fails to construct the data object.

Sequence Name is derived from the path, not configured. It is image_dir.split('/')[-2] — the second-to-last component of image_dir. A trailing slash or a flat image directory shifts which component is picked, so two sources can collide under one name. Lay out image_dir so that component is the sequence identifier you want.

data.mapping values are LISTS of aliases, not strings. This is the single most destructive thing to get wrong: the file is a YAML list of single-key dicts whose value is a list of source names that fold into that canonical class.

- bicycle:
    - Bicycle
    - Motorcycle
    - bicycle
    - twowheeler
- car:
    - car
    - Heavy Truck
    - Vehicle

construct_category_map stores the value verbatim (cat_map[k] = v), so writing - car: car — a bare string — yields a value that downstream code iterates character by character. Class matching then fails for every box, and the run still exits 0: the result is TP=0, FN=0, every prediction counted a false positive, and mAP: 0.0, with no error and no warning.

The tell is a perfect-looking run with all-zero metrics. Sanity-check by scoring a ground-truth set against a copy of itself — with a correct mapping that returns TP = every box and mAP: 1.0; anything else means the mapping, not the model.

data.mapping is required. The Hydra schema marks it mandatory even though the underlying category-map builder can derive classes from the label directory when it is absent. Supply the YAML.

Two different conf_threshold defaults. The dataclass default is 0.5, the shipped spec template uses 0.0. Whichever you rely on, set it explicitly — an unset value silently changes which predictions are scored.

is_internal: true is destructive to the report. It drops every class except person and appends a Summary row. Leave it false unless you specifically want the internal person-only KPI.

Troubleshooting

<key> not found in kpi_sources: every source entry needs all three of image_dir, ground_truth_ann_path, inference_ann_path.

Paths not found inside Docker: use a RUN_ROOT mount where host and container paths are identical, and confirm the images and annotation directories are under that mount.

Empty or all-zero metrics: usually conf_threshold above the model's score range, or an input_format that does not match the annotations on disk.

wandb errors or hangs: set visualize.platform: local to write a PR-curve plot instead of logging to wandb.

Files

LICENSE

Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.


                                 Apache License
                           Version 2.0, January 2004
                        http://www.apache.org/licenses/

   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION

   1. Definitions.

      "License" shall mean the terms and conditions for use, reproduction,
      and distribution as defined by Sections 1 through 9 of this document.

      "Licensor" shall mean the copyright owner or entity authorized by
      the copyright owner that is granting the License.

      "Legal Entity" shall mean the union of the acting entity and all
      other entities that control, are controlled by, or are under common
      control with that entity. For the purposes of this definition,
      "control" means (i) the power, direct or indirect, to cause the
      direction or management of such entity, whether by contract or
      otherwise, or (ii) ownership of fifty percent (50%) or more of the
      outstanding shares, or (iii) beneficial ownership of such entity.

      "You" (or "Your") shall mean an individual or Legal Entity
      exercising permissions granted by this License.

      "Source" form shall mean the preferred form for making modifications,
      including but not limited to software source code, documentation
      source, and configuration files.

      "Object" form shall mean any form resulting from mechanical
      transformation or translation of a Source form, including but
      not limited to compiled object code, generated documentation,
      and conversions to other media types.

      "Work" shall mean the work of authorship, whether in Source or
      Object form, made available under the License, as indicated by a
      copyright notice that is included in or attached to the work
      (an example is provided in the Appendix below).

      "Derivative Works" shall mean any work, whether in Source or Object
      form, that is based on (or derived from) the Work and for which the
      editorial revisions, annotations, elaborations, or other modifications
      represent, as a whole, an original work of authorship. For the purposes
      of this License, Derivative Works shall not include works that remain
      separable from, or merely link (or bind by name) to the interfaces of,
      the Work and Derivative Works thereof.

      "Contribution" shall mean any work of authorship, including
      the original version of the Work and any modifications or additions
      to that Work or Derivative Works thereof, that is intentionally
      submitted to Licensor for inclusion in the Work by the copyright owner
      or by an individual or Legal Entity authorized to submit on behalf of
      the copyright owner. For the purposes of this definition, "submitted"
      means any form of electronic, verbal, or written communication sent
      to the Licensor or its representatives, including but not limited to
      communication on electronic mailing lists, source code control systems,
      and issue tracking systems that are managed by, or on behalf of, the
      Licensor for the purpose of discussing and improving the Work, but
      excluding communication that is conspicuously marked or otherwise
      designated in writing by the copyright owner as "Not a Contribution."

      "Contributor" shall mean Licensor and any individual or Legal Entity
      on behalf of whom a Contribution has been received by Licensor and
      subsequently incorporated within the Work.

   2. Grant of Copyright License. Subject to the terms and conditions of
      this License, each Contributor hereby grants to You a perpetual,
      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
      copyright license to reproduce, prepare Derivative Works of,
      publicly display, publicly perform, sublicense, and distribute the
      Work and such Derivative Works in Source or Object form.

   3. Grant of Patent License. Subject to the terms and conditions of
      this License, each Contributor hereby grants to You a perpetual,
      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
      (except as stated in this section) patent license to make, have made,
      use, offer to sell, sell, import, and otherwise transfer the Work,
      where such license applies only to those patent claims licensable
      by such Contributor that are necessarily infringed by their
      Contribution(s) alone or by combination of their Contribution(s)
      with the Work to which such Contribution(s) was submitted. If You
      institute patent litigation against any entity (including a
      cross-claim or counterclaim in a lawsuit) alleging that the Work
      or a Contribution incorporated within the Work constitutes direct
      or contributory patent infringement, then any patent licenses
      granted to You under this License for that Work shall terminate
      as of the date such litigation is filed.

   4. Redistribution. You may reproduce and distribute copies of the
      Work or Derivative Works thereof in any medium, with or without
      modifications, and in Source or Object form, provided that You
      meet the following conditions:

      (a) You must give any other recipients of the Work or
          Derivative Works a copy of this License; and

      (b) You must cause any modified files to carry prominent notices
          stating that You changed the files; and

      (c) You must retain, in the Source form of any Derivative Works
          that You distribute, all copyright, patent, trademark, and
          attribution notices from the Source form of the Work,
          excluding those notices that do not pertain to any part of
          the Derivative Works; and

      (d) If the Work includes a "NOTICE" text file as part of its
          distribution, then any Derivative Works that You distribute must
          include a readable copy of the attribution notices contained
          within such NOTICE file, excluding those notices that do not
          pertain to any part of the Derivative Works, in at least one
          of the following places: within a NOTICE text file distributed
          as part of the Derivative Works; within the Source form or
          documentation, if provided along with the Derivative Works; or,
          within a display generated by the Derivative Works, if and
          wherever such third-party notices normally appear. The contents
          of the NOTICE file are for informational purposes only and
          do not modify the License. You may add Your own attribution
          notices within Derivative Works that You distribute, alongside
          or as an addendum to the NOTICE text from the Work, provided
          that such additional attribution notices cannot be construed
          as modifying the License.

      You may add Your own copyright statement to Your modifications and
      may provide additional or different license terms and conditions
      for use, reproduction, or distribution of Your modifications, or
      for any such Derivative Works as a whole, provided Your use,
      reproduction, and distribution of the Work otherwise complies with
      the conditions stated in this License.

   5. Submission of Contributions. Unless You explicitly state otherwise,
      any Contribution intentionally submitted for inclusion in the Work
      by You to the Licensor shall be under the terms and conditions of
      this License, without any additional terms or conditions.
      Notwithstanding the above, nothing herein shall supersede or modify
      the terms of any separate license agreement you may have executed
      with Licensor regarding such Contributions.

   6. Trademarks. This License does not grant permission to use the trade
      names, trademarks, service marks, or product names of the Licensor,
      except as required for reasonable and customary use in describing the
      origin of the Work and reproducing the content of the NOTICE file.

   7. Disclaimer of Warranty. Unless required by applicable law or
      agreed to in writing, Licensor provides the Work (and each
      Contributor provides its Contributions) on an "AS IS" BASIS,
      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
      implied, including, without limitation, any warranties or conditions
      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
      PARTICULAR PURPOSE. You are solely responsible for determining the
      appropriateness of using or redistributing the Work and assume any
      risks associated with Your exercise of permissions under this License.

   8. Limitation of Liability. In no event and under no legal theory,
      whether in tort (including negligence), contract, or otherwise,
      unless required by applicable law (such as deliberate and grossly
      negligent acts) or agreed to in writing, shall any Contributor be
      liable to You for damages, including any direct, indirect, special,
      incidental, or consequential damages of any character arising as a
      result of this License or out of the use or inability to use the
      Work (including but not limited to damages for loss of goodwill,
      work stoppage, computer failure or malfunction, or any and all
      other commercial damages or losses), even if such Contributor
      has been advised of the possibility of such damages.

   9. Accepting Warranty or Additional Liability. While redistributing
      the Work or Derivative Works thereof, You may choose to offer,
      and charge a fee for, acceptance of support, warranty, indemnity,
      or other liability obligations and/or rights consistent with this
      License. However, in accepting such obligations, You may act only
      on Your own behalf and on Your sole responsibility, not on behalf
      of any other Contributor, and only if You agree to indemnify,
      defend, and hold each Contributor harmless for any liability
      incurred by, or claims asserted against, such Contributor by reason
      of your accepting any such warranty or additional liability.

   END OF TERMS AND CONDITIONS

   APPENDIX: How to apply the Apache License to your work.

      To apply the Apache License to your work, attach the following
      boilerplate notice, with the fields enclosed by brackets "[]"
      replaced with your own identifying information. (Don't include
      the brackets!)  The text should be enclosed in the appropriate
      comment syntax for the file format. We also recommend that a
      file or class name and description of purpose be included on the
      same "printed page" as the copyright notice for easier
      identification within third-party archives.

   Copyright [yyyy] [name of copyright owner]

   Licensed under the Apache License, Version 2.0 (the "License");
   you may not use this file except in compliance with the License.
   You may obtain a copy of the License at

       http://www.apache.org/licenses/LICENSE-2.0

   Unless required by applicable law or agreed to in writing, software
   distributed under the License is distributed on an "AS IS" BASIS,
   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   See the License for the specific language governing permissions and
   limitations under the License.

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Declares allowed tools, Needs a command-line tool, Needs Python

The author says: Requires docker, nvidia-container-toolkit, and the TAO data-services container pinned in versions.yaml.

Security

What our scanner noticed· Medium risk

  • Large base64-encoded blobs
  • References to credential files
  • Scripts that your agent may run

This skill includes scripts that your agent may run. Read them before you use it.

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
NVIDIA Corporation
Publisher
NVIDIA (Official)
Source
NVIDIA/skills · skills/tao-analyze-detection-kpi · commit fce3cca
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE-APACHE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by NVIDIA Corporation or NVIDIA.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download tao-analyze-detection-kpi.zip