Tao analyze gaps od map

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.yaml

Inputs

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-skills-external 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=nvcr.io/nvstaging/tao/tao-toolkit-ds:2026.8.10-rc-26-multiarch  # versions-key: images.tao_toolkit.data_services_nightly

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

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.

Preflight

1. Verify Docker access:

docker info > /dev/null

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

DS_IMAGE=nvcr.io/nvstaging/tao/tao-toolkit-ds:2026.8.10-rc-26-multiarch  # versions-key: images.tao_toolkit.data_services_nightly
docker image inspect "$DS_IMAGE" > /dev/null || docker pull "$DS_IMAGE"

3. Confirm RUN_ROOT contains the spec, both annotation sources, and the image directory. Mount RUN_ROOT to 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 writes as root. Chown back with docker run --rm -v "$RUN_ROOT:$RUN_ROOT" alpine chown -R "$(id -u):$(id -g)" "$RESULTS_DIR".