Tao Analyze Gaps Od Map
NVIDIAOfficialCreated by NVIDIA Corporation
- Skill
- Review and audit
- Software development
- Apache-2.0
- Medium risk
- Has scripts
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
The TAO Analyze Gaps OD mAP skill identifies weak images in an object detection dataset by evaluating model performance against ground truth. It computes metrics such as TP, FP, FN, and AP50 on a per-image and per-class basis. By applying configurable thresholds, the skill flags images where model performance is insufficient, providing actionable data for downstream mining and model improvement workflows. It requires pre-existing inference annotations and ground truth data to function.
What it does
- Compares ground-truth and inference annotations to identify model performance gaps.
- Calculates per-image and per-class metrics including recall, precision, and AP50.
- Generates a list of weak images based on user-defined performance thresholds.
- Produces detailed parquet reports for box-level gaps and image-level metrics.
- Validates configuration specifications to ensure correct input formats and pathing.
- Outputs a JSON gap report summarizing FP/FN counts and run settings.
When to use it
- When you need to identify images where an object detection model is underperforming.
- When preparing data for active learning or image mining workflows.
- When you need to audit model predictions against ground truth annotations.
- When you need to generate a quantitative report on model weaknesses per class.
Advantages
- Enables targeted data collection by identifying specific weak images.
- Supports both KITTI and COCO annotation formats.
- Provides granular metrics for per-class performance analysis.
- Ensures reproducibility by linking output artifacts with the specific experiment configuration.
Usage examples
- “Analyze OD gaps for my current model predictions.”
- The agent will guide you to configure the object_detection.yaml spec with your annotation paths and run the gap analysis container to identify weak images.
- “Find weak OD images where car recall is below 0.9.”
- The agent will help you set the weak_thresholds in your spec to include a car recall threshold of 0.9 and execute the analysis to filter the dataset.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/nvidia/tao-analyze-gaps-od-map/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip tao-analyze-gaps-od-map.zip -d .agents/skills
Documentation by NVIDIA Corporation
TAO Analyze Gaps OD mAP
Use this skill to run TAO Data Services object-detection gap analysis. The skill compares ground-truth and inference annotations, computes per-image per-class TP/FP/FN/AP50 metrics, and identifies weak images where any class metric falls below its threshold. It does not run inference; upstream steps must produce the inference annotations first.
The container entrypoint is:
gap_analysis object_detection -e /absolute/path/to/object_detection.yamlInputs
Required spec fields:
| Field | Meaning |
|---|---|
ground_truth_ann_path | KITTI label directory or COCO .json with ground-truth boxes. |
inference_ann_path | KITTI label directory or COCO .json with model predictions. |
images_dir | Root image directory. Establishes the full image universe including unannotated images. |
results_dir | Output directory for all artifacts. |
kpi | Identifier tag written to every output row. |
input_format | kitti or coco. Must be declared explicitly; never inferred from the path. |
Common optional fields:
| Field | Default | Meaning |
|---|---|---|
iou_threshold | 0.5 | IoU at or above which a prediction is accepted as a true positive. |
conf_threshold | 0.0 | Predictions below this confidence are dropped before matching. |
min_area | 0 | Boxes whose pixel area (w × h) is strictly below this value are discarded. |
class_mapping | {} | Maps raw annotation label strings to canonical class names. Absent labels are kept as-is. |
weak_thresholds | {} | Per-class thresholds as {class_name: {recall, precision, ap50}}. Absent keys fall back to the default_*_threshold values. Reference ITS defaults: car 0.99, bicycle 0.7, person 0.7 — a strict gate on the abundant, well-learned class and looser gates on the rare ones the loop exists to improve. |
default_recall_threshold | 0.5 | Fallback recall threshold for classes not listed in weak_thresholds. |
default_precision_threshold | 0.0 | Fallback precision threshold. Set to 0.0 to disable precision-based weak selection. |
default_ap50_threshold | 0.5 | Fallback for classes absent from weak_thresholds. Set 0.0 so unlisted classes never mark an image weak — the reference filter had no fallback, and leaving TAO DS's 0.5 in place silently gates every class you did not list. |
Do not hand-write the spec. Copy the template and fill in the nulls — every
tuning value it already carries is the one this stage wants — then validate:
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"verify rejects the spellings that fail — uppercase input_format, relative or
missing paths, a weak_thresholds entry that is a bare number rather than a
mapping — and reports every gated class plus the default_* fallbacks, so the
selection criteria behind a weak set are recoverable from the run's output. It
warns when a fallback is above zero, since that gates classes you did not list.
Quick Start
Run from the tao-skill-bank repo root.
Write the spec into the results directory. The run emits four artifacts and does not retain the spec, so a completed gap analysis otherwise cannot tell you which thresholds produced its weak set — and that weak set sizes the mining budget downstream. Keeping them together makes the selection criteria recoverable from the run alone.
RESULTS_DIR=/absolute/path/for/this/run # results_dir in the spec
SPEC="$RESULTS_DIR/object_detection.yaml" # spec lives beside its outputs
RUN_ROOT=/absolute/path/that/contains/annotations/images/and/results
GPU_COUNT=1
DS_IMAGE="$(scripts/resolve_versions_key.py images.tao_toolkit.data_services)"
RUNTIME_HOME="$RUN_ROOT/.tao-runtime/home"
mkdir -p "$RUNTIME_HOME/.cache"
docker run --rm --gpus "$GPU_COUNT" --shm-size=8g --network=host \
--user "$(id -u):$(id -g)" \
-e USER="$(id -un)" -e LOGNAME="$(id -un)" \
-e HOME="$RUNTIME_HOME" -e XDG_CACHE_HOME="$RUNTIME_HOME/.cache" \
-v "$RUN_ROOT:$RUN_ROOT" \
-w "$RUN_ROOT" \
"$DS_IMAGE" \
gap_analysis object_detection -e "$SPEC"Keep --user and the identity env block above. TAO DS images call getpass.getuser() at startup; without USER/LOGNAME they fail with KeyError: 'getpwuid(): uid not found: <uid>' because the UID has no /etc/passwd entry. Do not work around it by running the container as root; that leaves root-owned outputs. This follows the launch contract in tao-run-on-docker.
Preflight
Verify Docker access:
docker info > /dev/nullResolve 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 "$DS_IMAGE"Confirm
RUN_ROOTcontains the spec, both annotation sources, and the image directory. MountRUN_ROOTto the same absolute path inside Docker.
Outputs
| Artifact | Location | Contents |
|---|---|---|
| FP/FN box gaps | results_dir/box_gaps.parquet | One row per unmatched box: kpi, image_id, filepath, class, gap_type (FP/FN), bbox, confidence, best_iou. |
| Per-image metrics | results_dir/image_metrics.parquet | Per-image per-class: tp, fp, fn, precision, recall, ap50. |
| Weak images | results_dir/weak_images.parquet | Images where any class metric falls below threshold: filepath, weak_classes, weak_recall, weak_precision, weak_ap50. Feed this into tao-mine-od-images. |
| Gap report | results_dir/gap_report.json | FP/FN counts by type and class, plus run settings. |
All four artifacts are always written, even when no gaps are found.
Troubleshooting
The subtask object_detection requires -e/--experiment_spec_file: rerun with gap_analysis object_detection -e "$SPEC".
Input path not found inside Docker: use a RUN_ROOT mount where host and container paths are identical.
input_format error: set input_format: kitti or input_format: coco explicitly — it is never inferred from the path.
weak_images.parquet is empty: all class metrics are above their thresholds. Lower default_recall_threshold / default_ap50_threshold or add per-class entries to weak_thresholds.
Output directory not writable after Docker exits: the container ran without --user, so it wrote as root. Relaunch with the --user and identity env block shown above.
Files
- ATTRIBUTION.mdAdded by AutoGineer939 B
- BENCHMARK.md6.9 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md7.2 KiB
- assets/default_object_detection.yaml1.2 KiB
- config/skillspector-baseline.yaml1.3 KiB
- evals/evals.json948 B
- references/skill_info.yaml660 B
- scripts/verify_object_detection_spec.pyScript8.8 KiB
- skill-card.md4.9 KiB
- skill.oms.sig5.5 KiB
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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
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Attribution and licence
- Created by
- NVIDIA Corporation
- Publisher
- NVIDIA (Official)
- Source
- NVIDIA/skills · skills/tao-analyze-gaps-od-map · commit fce3cca
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE-APACHE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
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