TAO Object Detection Gap Analysis

What it does

When your agent uses it

• Finding images where a detector underperforms for a class
• Selecting weak images for another training round
• Setting per-class recall and AP50 thresholds
• Checking KITTI or COCO predictions against ground truth

Example prompts

• “Analyze OD gaps for my COCO ground truth and model predictions.”
• “Find weak images where car recall is below 0.99.”
• “Run mAP gap analysis and write the results to the results directory.”

Requirements

• docker and nvidia-container-toolkit
• The TAO data-services container pinned in versions.yaml
• Inference annotations from an earlier step
• Compatibility (from SKILL.md): Requires docker, nvidia-container-toolkit, and the TAO data-services container pinned in versions.yaml.
• Pre-approved tools (allowed-tools): Read, Bash

Workflow steps

3 steps, taken from the first numbered list in SKILL.md.

1. 1Verify Docker access
2. 2Resolve and pull the data-services image if needed
3. 3Confirm RUN_ROOT contains the spec, both annotation sources, and the image directory. Mount RUN_ROOT to the same absolute path inside…

What it can do on your machine

Read from SKILL.md and the folder at commit 14a98ae. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves these tools, so the agent can use them without asking each time:

  • Read
  • Bash

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Ships 1 file in scripts/ (Python), which the agent can run.

  Shell commands in SKILL.md call:

  • docker
  • python3

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  No URLs in SKILL.md. Its commands use docker, which can reach the network depending on how they are called.

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

• Compatibility

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

  From compatibility in the SKILL.md frontmatter.

Context cost

TAO Object Detection Gap Analysis loads about 1.8k tokens when it runs, and up to ~1.9k if it reads all its reference files. Until then it costs about 98 tokens; SKILL.md has 671 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~98

When it runs · the whole SKILL.md, loaded when a task matches

~1.8k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~1.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

• NotePre-approves every shell command (allowed-tools: Bash)SKILL.md

  allowed-tools: Read, Bash

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from NVIDIA/skills at commit 14a98ae, republished under its Apache-2.0 licence (© NVIDIA). 671 words, ~1,756 tokens.

name

tao-analyze-gaps-od-map

description

Run TAO Data Services object-detection gap analysis from ground-truth and inference annotations. Use when an object detection workflow needs to identify weak images by comparing model predictions against ground truth using per-class recall, precision, and AP50 thresholds. Use when the user asks to "analyze OD gaps", "find weak OD images", or "run mAP gap analysis".

allowed-tools

Read, Bash

compatibility

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

license

Apache-2.0

metadata.author

NVIDIA Corporation

metadata.version

0.1.0

tags

tao, data, gap-analysis, object-detection, mAP, rcca

gap_analysis object_detection -e /absolute/path/to/object_detection.yaml

cp skills/data/tao-analyze-gaps-od-map/assets/default_object_detection.yaml "$SPEC"
# fill ground_truth_ann_path, inference_ann_path, images_dir, results_dir, kpi, input_format
python3 skills/data/tao-analyze-gaps-od-map/scripts/verify_object_detection_spec.py --spec "$SPEC"

RESULTS_DIR=/absolute/path/for/this/run          # results_dir in the spec
SPEC="$RESULTS_DIR/object_detection.yaml"        # spec lives beside its outputs
RUN_ROOT=/absolute/path/that/contains/annotations/images/and/results
GPU_COUNT=1

DS_IMAGE=nvcr.io/nvidia/tao/tao-toolkit:7.2.0-data-services  # versions-key: images.tao_toolkit.data_services

docker run --rm --gpus "$GPU_COUNT" --shm-size=8g --network=host \
  -v "$RUN_ROOT:$RUN_ROOT" \
  -w "$RUN_ROOT" \
  "$DS_IMAGE" \
  gap_analysis object_detection -e "$SPEC"

docker info > /dev/null

DS_IMAGE=nvcr.io/nvidia/tao/tao-toolkit:7.2.0-data-services  # versions-key: images.tao_toolkit.data_services
docker image inspect "$DS_IMAGE" > /dev/null || docker pull "$DS_IMAGE"

© NVIDIA, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 8 other files (scripts, references, assets) in skills/tao-analyze-gaps-od-map of NVIDIA/skills.

• SKILL.md
• BENCHMARK.md
• assets/default_object_detection.yaml
• config/skillspector-baseline.yaml
• evals/evals.json
• references/skill_info.yaml
• scripts/verify_object_detection_spec.py
• skill-card.md
• skill.oms.sig

Open the folder on GitHubat commit 14a98ae