diff --git a/README.md b/README.md
index de7ca9d..091da17 100644
--- a/README.md
+++ b/README.md
@@ -83,6 +83,7 @@ YOLOv8n was validated with a real OpenCV image-input benchmark: InferEdgeRuntime
TensorRT Jetson was 4.6x faster than ONNX Runtime CPU in this real image input benchmark.
The benchmark uses end-to-end Runtime latency, not trtexec GPU-only latency.
The full pipeline portfolio summary is available at [docs/portfolio/inferedge_pipeline_portfolio.md](docs/portfolio/inferedge_pipeline_portfolio.md), and the detailed Runtime comparison report is available at [docs/portfolio/runtime_compare_yolov8n.md](docs/portfolio/runtime_compare_yolov8n.md).
+The YOLOv8 COCO subset accuracy demo is documented in [docs/portfolio/yolov8_coco_subset_evaluation.md](docs/portfolio/yolov8_coco_subset_evaluation.md).
## Local Studio Demo Evidence
@@ -100,6 +101,7 @@ Verified demo fixture values:
Studio reports this as a `4.57x` TensorRT speedup for the bundled demo pair.
AIGuard remains optional in this local Studio path; if Guard evidence is not loaded, the deployment decision explains that the Lab comparison is available but diagnosis evidence is not provided.
+The same demo flow also surfaces a small `yolov8_coco` evaluation report summary: 10 images, 89 ground-truth boxes, mAP@50 `0.1410`, precision `0.2941`, recall `0.1685`, structural validation `passed`.
---
diff --git a/docs/portfolio/inferedge_pipeline_status.md b/docs/portfolio/inferedge_pipeline_status.md
index 077eb6a..05fa078 100644
--- a/docs/portfolio/inferedge_pipeline_status.md
+++ b/docs/portfolio/inferedge_pipeline_status.md
@@ -96,6 +96,7 @@ The current cross-repository loop is covered by documentation, fixtures, and smo
- AIGuard worker provenance mismatch diagnosis
- Lab deployment decision/report evidence smoke for AIGuard worker provenance diagnosis
- Local Studio local-first workflow UI for viewing Forge -> Runtime -> Lab -> optional AIGuard state, creating in-memory analyze jobs, importing Runtime result JSON, replaying bundled demo evidence, comparing backends, and inspecting Lab-owned deployment decision context
+- YOLOv8 COCO subset evaluation report generated from 10 local images and 89 converted COCO-style person annotations, with mAP@50 0.1410, precision 0.2941, recall 0.1685, and structural validation passed
This means the current product boundary is testable without running the production worker infrastructure.
@@ -125,6 +126,7 @@ Demo readiness: `scripts/demo_pipeline_full.sh` now provides a guided end-to-end
- Runtime compare-key identity polish for manifest-backed engine artifacts
- Guided end-to-end demo entrypoint for portfolio and interview walkthroughs
- Local Studio at `/studio` for a local-first browser view of Run / Import / Demo Evidence / Compare / Decision / Jetson Helper workflows
+- Contract/preset validation demo with `yolov8_coco`, COCO annotation loading, simplified accuracy metrics, structural validation, and JSON/Markdown/HTML report fixtures
- Cross-repo fixture compatibility across Forge, Runtime, Lab, and AIGuard
- Rule/evidence based provenance mismatch diagnosis
diff --git a/docs/portfolio/inferedge_portfolio_submission.md b/docs/portfolio/inferedge_portfolio_submission.md
index 3d29176..08c7563 100644
--- a/docs/portfolio/inferedge_portfolio_submission.md
+++ b/docs/portfolio/inferedge_portfolio_submission.md
@@ -112,6 +112,7 @@ Recent validation evidence:
- Runtime compare-key identity polish: InferEdgeRuntime now preserves Forge manifest source model identity for compare naming. If `manifest.source_model.path` is `models/onnx/yolov8n.onnx` and the explicit TensorRT artifact path is `model.engine`, Runtime can keep `compare_model_name=yolov8n` and `compare_key=yolov8n__b1__h640w640__fp32`.
- Guided demo entrypoint: `scripts/demo_pipeline_full.sh` summarizes the full Forge -> Runtime -> Lab -> optional AIGuard flow and can print the Jetson TensorRT Runtime command without claiming production worker or SaaS readiness.
- Local Studio demo evidence: `/studio` can load bundled ONNX Runtime CPU and TensorRT Jetson Runtime result fixtures from `examples/studio_demo`, keep the demo pair selectable in Recent jobs while the local server process is alive, and show TensorRT Jetson vs ONNX Runtime CPU comparison in the browser. The fixture-backed evidence records ONNX Runtime CPU at mean 45.4299 ms / p99 49.2128 ms / 22.0119 FPS and TensorRT Jetson at mean 9.9375 ms / p99 15.5231 ms / 100.6293 FPS, a 4.57x TensorRT speedup for this demo pair.
+- YOLOv8 COCO subset evaluation: a 10-image local person-detection subset with 89 ground-truth boxes is converted into a COCO-style annotation fixture and evaluated through the `yolov8_coco` preset. The generated report records mAP@50 0.1410, precision 0.2941, recall 0.1685, and structural validation passed. This is documented as subset workflow evidence, not a full COCO benchmark claim.
The direct Runtime execution result includes `deployment_decision`. Its `unknown` value is expected before Lab compare/report because the worker response has not yet been compared by Lab.
diff --git a/docs/portfolio/yolov8_coco_subset_evaluation.md b/docs/portfolio/yolov8_coco_subset_evaluation.md
new file mode 100644
index 0000000..833a052
--- /dev/null
+++ b/docs/portfolio/yolov8_coco_subset_evaluation.md
@@ -0,0 +1,43 @@
+# YOLOv8 COCO Subset Evaluation Demo
+
+This document records a small local-first accuracy evaluation demo for InferEdgeLab.
+It is not a full COCO benchmark and should not be presented as production model validation.
+
+## Scope
+
+- Preset: `yolov8_coco`
+- Model: YOLOv8n ONNX Runtime CPU
+- Demo input: 10 local person-detection images
+- Annotation source: local YOLO txt labels converted into a compact COCO-style annotation fixture
+- Raw images: intentionally not committed
+- Annotation fixture: `examples/validation_demo/subset/yolov8_coco_subset_annotations.json`
+- Evaluation report: `examples/validation_demo/subset/yolov8_coco_subset_evaluation.json`
+
+## Result
+
+| Metric | Value |
+|---|---:|
+| Samples | 10 |
+| Ground-truth boxes | 89 |
+| Post-NMS detections checked | 51 |
+| mAP@50 | 0.1410 |
+| mAP@50-95 | 0.0873 |
+| Precision | 0.2941 |
+| Recall | 0.1685 |
+| F1 score | 0.2143 |
+| Structural validation | passed |
+| Contract input shape | passed |
+
+## Interpretation
+
+This demo proves that InferEdgeLab can load COCO-style annotations, run the YOLOv8 detection evaluator, compute simplified accuracy metrics, validate detection output structure, and emit JSON/Markdown/HTML reports.
+The numbers are intentionally documented as a small subset result only.
+They are useful as portfolio workflow evidence, not as a claim of full COCO accuracy.
+
+The relatively low recall is expected for this tiny local subset because the images are night beach/crowd scenes with many small person boxes.
+That is useful for the portfolio: it shows that the validation pipeline records uncomfortable evidence instead of hiding it.
+
+## Local Studio Link
+
+Local Studio's `Load Demo Evidence` flow now returns this evaluation report summary together with the existing ONNX Runtime CPU vs TensorRT Jetson latency pair.
+The Studio path remains local-first and does not upload raw images or add database, queue, auth, or production SaaS features.
diff --git a/examples/validation_demo/subset/yolov8_coco_subset_annotations.json b/examples/validation_demo/subset/yolov8_coco_subset_annotations.json
new file mode 100644
index 0000000..a7a6b1d
--- /dev/null
+++ b/examples/validation_demo/subset/yolov8_coco_subset_annotations.json
@@ -0,0 +1,1234 @@
+{
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+ ],
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+ "bbox": [
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+ 29.361,
+ 57.45
+ ],
+ "area": 1686.755,
+ "iscrowd": 0
+ },
+ {
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+ "image_id": 10,
+ "category_id": 1,
+ "bbox": [
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+ 19.421,
+ 44.031
+ ],
+ "area": 855.108,
+ "iscrowd": 0
+ },
+ {
+ "id": 88,
+ "image_id": 10,
+ "category_id": 1,
+ "bbox": [
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+ 743.25,
+ 24.601,
+ 58.28
+ ],
+ "area": 1433.745,
+ "iscrowd": 0
+ },
+ {
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+ "image_id": 10,
+ "category_id": 1,
+ "bbox": [
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+ 747.14,
+ 16.831,
+ 51.8
+ ],
+ "area": 871.832,
+ "iscrowd": 0
+ }
+ ]
+}
diff --git a/examples/validation_demo/subset/yolov8_coco_subset_evaluation.html b/examples/validation_demo/subset/yolov8_coco_subset_evaluation.html
new file mode 100644
index 0000000..f43b2d4
--- /dev/null
+++ b/examples/validation_demo/subset/yolov8_coco_subset_evaluation.html
@@ -0,0 +1,25 @@
+
+InferEdge Evaluation Report# InferEdge Evaluation Report
+
+- preset: `yolov8_coco`
+- engine: `onnxruntime`
+- device: `cpu`
+- samples: `10`
+- accuracy status: `evaluated`
+- contract input shape: `passed`
+- structural validation: `passed`
+- deployment signal: `review`
+
+## Metrics
+- map50: `0.14097840361885305`
+- map50_95: `0.08728567780534073`
+- f1_score: `0.21428571428571427`
+- precision: `0.29411764705882354`
+- recall: `0.16853932584269662`
+
+## Notes
+- Detection evaluation uses image directory traversal.
+- YOLOv8 postprocessing supports single-output and split boxes/scores output layouts.
+- Accuracy uses YOLO txt labels or COCO annotations when provided.
+- When annotations are missing, InferEdge records accuracy_skipped and structural validation only.
+
diff --git a/examples/validation_demo/subset/yolov8_coco_subset_evaluation.json b/examples/validation_demo/subset/yolov8_coco_subset_evaluation.json
new file mode 100644
index 0000000..0ca59b0
--- /dev/null
+++ b/examples/validation_demo/subset/yolov8_coco_subset_evaluation.json
@@ -0,0 +1,312 @@
+{
+ "report_role": "inferedge-evaluation-report",
+ "generated_at": "2026-05-01T08:08:02Z",
+ "preset": {
+ "name": "yolov8_coco",
+ "task": "object_detection",
+ "description": "YOLOv8 object detection on COCO-style labels.",
+ "input_shape": [
+ 1,
+ 3,
+ 640,
+ 640
+ ],
+ "input_format": "NCHW_RGB_FLOAT32_0_1",
+ "output_type": "yolov8_detection",
+ "output_shape": [
+ 1,
+ 84,
+ 8400
+ ],
+ "labels": [
+ "person",
+ "bicycle",
+ "car",
+ "motorcycle",
+ "airplane",
+ "bus",
+ "train",
+ "truck",
+ "boat",
+ "traffic light",
+ "fire hydrant",
+ "stop sign",
+ "parking meter",
+ "bench",
+ "bird",
+ "cat",
+ "dog",
+ "horse",
+ "sheep",
+ "cow",
+ "elephant",
+ "bear",
+ "zebra",
+ "giraffe",
+ "backpack",
+ "umbrella",
+ "handbag",
+ "tie",
+ "suitcase",
+ "frisbee",
+ "skis",
+ "snowboard",
+ "sports ball",
+ "kite",
+ "baseball bat",
+ "baseball glove",
+ "skateboard",
+ "surfboard",
+ "tennis racket",
+ "bottle",
+ "wine glass",
+ "cup",
+ "fork",
+ "knife",
+ "spoon",
+ "bowl",
+ "banana",
+ "apple",
+ "sandwich",
+ "orange",
+ "broccoli",
+ "carrot",
+ "hot dog",
+ "pizza",
+ "donut",
+ "cake",
+ "chair",
+ "couch",
+ "potted plant",
+ "bed",
+ "dining table",
+ "toilet",
+ "tv",
+ "laptop",
+ "mouse",
+ "remote",
+ "keyboard",
+ "cell phone",
+ "microwave",
+ "oven",
+ "toaster",
+ "sink",
+ "refrigerator",
+ "book",
+ "clock",
+ "vase",
+ "scissors",
+ "teddy bear",
+ "hair drier",
+ "toothbrush"
+ ],
+ "thresholds": {
+ "score": 0.25,
+ "iou": 0.5
+ },
+ "accuracy": {
+ "primary_metric": "map50",
+ "secondary_metrics": [
+ "precision",
+ "recall",
+ "f1_score",
+ "map50_95"
+ ],
+ "annotation_formats": [
+ "coco",
+ "yolo_txt"
+ ]
+ }
+ },
+ "model_contract": {
+ "contract_version": "1",
+ "task": "object_detection",
+ "preset": "yolov8_coco",
+ "labels": [
+ "person",
+ "bicycle",
+ "car",
+ "motorcycle",
+ "airplane",
+ "bus",
+ "train",
+ "truck",
+ "boat",
+ "traffic light",
+ "fire hydrant",
+ "stop sign",
+ "parking meter",
+ "bench",
+ "bird",
+ "cat",
+ "dog",
+ "horse",
+ "sheep",
+ "cow",
+ "elephant",
+ "bear",
+ "zebra",
+ "giraffe",
+ "backpack",
+ "umbrella",
+ "handbag",
+ "tie",
+ "suitcase",
+ "frisbee",
+ "skis",
+ "snowboard",
+ "sports ball",
+ "kite",
+ "baseball bat",
+ "baseball glove",
+ "skateboard",
+ "surfboard",
+ "tennis racket",
+ "bottle",
+ "wine glass",
+ "cup",
+ "fork",
+ "knife",
+ "spoon",
+ "bowl",
+ "banana",
+ "apple",
+ "sandwich",
+ "orange",
+ "broccoli",
+ "carrot",
+ "hot dog",
+ "pizza",
+ "donut",
+ "cake",
+ "chair",
+ "couch",
+ "potted plant",
+ "bed",
+ "dining table",
+ "toilet",
+ "tv",
+ "laptop",
+ "mouse",
+ "remote",
+ "keyboard",
+ "cell phone",
+ "microwave",
+ "oven",
+ "toaster",
+ "sink",
+ "refrigerator",
+ "book",
+ "clock",
+ "vase",
+ "scissors",
+ "teddy bear",
+ "hair drier",
+ "toothbrush"
+ ],
+ "input": {
+ "shape": [
+ 1,
+ 3,
+ 640,
+ 640
+ ],
+ "format": "NCHW_RGB_FLOAT32_0_1",
+ "name": "images",
+ "dtype": "float32",
+ "type": null
+ },
+ "output": {
+ "shape": [
+ 1,
+ 84,
+ 8400
+ ],
+ "format": "tensor",
+ "name": "output0",
+ "dtype": "float32",
+ "type": "yolov8_detection"
+ },
+ "thresholds": {
+ "score": 0.25,
+ "iou": 0.5
+ },
+ "metadata": {
+ "demo_case": "normal",
+ "note": "Small contract fixture for contract/preset validation demos."
+ }
+ },
+ "runtime_result": {
+ "engine": "onnxruntime",
+ "device": "cpu",
+ "sample_count": 10,
+ "model_input": {
+ "name": "images",
+ "dtype": "",
+ "shape": [
+ 1,
+ 3,
+ 640,
+ 640
+ ]
+ },
+ "actual_input_shape": [
+ 1,
+ 3,
+ 640,
+ 640
+ ]
+ },
+ "accuracy": {
+ "status": "evaluated",
+ "metrics": {
+ "map50": 0.14097840361885305,
+ "map50_95": 0.08728567780534073,
+ "f1_score": 0.21428571428571427,
+ "precision": 0.29411764705882354,
+ "recall": 0.16853932584269662
+ },
+ "reason": null
+ },
+ "contract_validation": {
+ "input_shape": {
+ "status": "passed",
+ "actual_shape": [
+ 1,
+ 3,
+ 640,
+ 640
+ ],
+ "expected_shape": [
+ 1,
+ 3,
+ 640,
+ 640
+ ]
+ },
+ "preset": "yolov8_coco",
+ "task": "object_detection"
+ },
+ "structural_validation": {
+ "status": "passed",
+ "checked": {
+ "image_count": 10,
+ "detection_count": 51,
+ "num_classes": 80
+ },
+ "issues": []
+ },
+ "latency_summary": {
+ "status": "not_provided"
+ },
+ "deployment_signal": {
+ "decision": "review",
+ "reason": "Accuracy evidence is available; compare and deployment policy still decide release."
+ },
+ "notes": [
+ "Detection evaluation uses image directory traversal.",
+ "YOLOv8 postprocessing supports single-output and split boxes/scores output layouts.",
+ "Accuracy uses YOLO txt labels or COCO annotations when provided.",
+ "When annotations are missing, InferEdge records accuracy_skipped and structural validation only."
+ ]
+}
diff --git a/examples/validation_demo/subset/yolov8_coco_subset_evaluation.md b/examples/validation_demo/subset/yolov8_coco_subset_evaluation.md
new file mode 100644
index 0000000..0e5bdf6
--- /dev/null
+++ b/examples/validation_demo/subset/yolov8_coco_subset_evaluation.md
@@ -0,0 +1,23 @@
+# InferEdge Evaluation Report
+
+- preset: `yolov8_coco`
+- engine: `onnxruntime`
+- device: `cpu`
+- samples: `10`
+- accuracy status: `evaluated`
+- contract input shape: `passed`
+- structural validation: `passed`
+- deployment signal: `review`
+
+## Metrics
+- map50: `0.14097840361885305`
+- map50_95: `0.08728567780534073`
+- f1_score: `0.21428571428571427`
+- precision: `0.29411764705882354`
+- recall: `0.16853932584269662`
+
+## Notes
+- Detection evaluation uses image directory traversal.
+- YOLOv8 postprocessing supports single-output and split boxes/scores output layouts.
+- Accuracy uses YOLO txt labels or COCO annotations when provided.
+- When annotations are missing, InferEdge records accuracy_skipped and structural validation only.
diff --git a/inferedgelab/core/detection_evaluator.py b/inferedgelab/core/detection_evaluator.py
index 9ade7b5..44688dc 100644
--- a/inferedgelab/core/detection_evaluator.py
+++ b/inferedgelab/core/detection_evaluator.py
@@ -982,7 +982,11 @@ def evaluate_detection_engine(
},
extra={
"engine_path": engine_path,
- "runtime_artifact_path": getattr(engine.runtime_paths, "runtime_artifact_path", None),
+ "runtime_artifact_path": getattr(
+ getattr(engine, "runtime_paths", None),
+ "runtime_artifact_path",
+ None,
+ ),
"image_files": image_files,
"accuracy_status": accuracy_status,
"accuracy_skip_reason": accuracy_skip_reason,
diff --git a/inferedgelab/studio/routes.py b/inferedgelab/studio/routes.py
index 5f9acb6..59196e9 100644
--- a/inferedgelab/studio/routes.py
+++ b/inferedgelab/studio/routes.py
@@ -21,10 +21,12 @@
STATIC_DIR = Path(__file__).resolve().parent / "static"
DEMO_EVIDENCE_DIR = Path(__file__).resolve().parents[2] / "examples" / "studio_demo"
+VALIDATION_DEMO_DIR = Path(__file__).resolve().parents[2] / "examples" / "validation_demo" / "subset"
DEMO_EVIDENCE_FILES = (
"onnxruntime_cpu_result.json",
"tensorrt_jetson_result.json",
)
+DEMO_EVALUATION_REPORT = "yolov8_coco_subset_evaluation.json"
DEMO_JOB_ID = "demo_yolov8n_trt_vs_onnx"
STATIC_ASSETS = {
"app.js": "application/javascript",
@@ -156,10 +158,11 @@ def studio_import(request: Request, payload: dict[str, Any] = Body(...)) -> dict
@router.get("/studio/api/demo-evidence", include_in_schema=False)
def studio_demo_evidence(request: Request) -> dict[str, Any]:
results = [_load_demo_result(file_name) for file_name in DEMO_EVIDENCE_FILES]
+ evaluation_report = _load_demo_evaluation_report()
imported_results = _get_imported_results(request)
imported_results.extend(results)
compare = _build_imported_compare_response(results[0], results[1])
- demo_job = _build_demo_job(results, compare)
+ demo_job = _build_demo_job(results, compare, evaluation_report)
_get_demo_jobs(request)[DEMO_JOB_ID] = demo_job
return {
"status": "loaded",
@@ -170,6 +173,7 @@ def studio_demo_evidence(request: Request) -> dict[str, Any]:
"results": results,
"compare_ready": True,
"compare": compare,
+ "evaluation_report": evaluation_report,
"deployment_decision": compare["deployment_decision"],
}
@@ -296,7 +300,38 @@ def _load_demo_result(file_name: str) -> dict[str, Any]:
return _with_compare_keys(result)
-def _build_demo_job(results: list[dict[str, Any]], compare: dict[str, Any]) -> dict[str, Any]:
+def _load_demo_evaluation_report() -> dict[str, Any]:
+ path = VALIDATION_DEMO_DIR / DEMO_EVALUATION_REPORT
+ try:
+ report = json.loads(path.read_text(encoding="utf-8"))
+ except OSError as exc:
+ raise HTTPException(status_code=500, detail=f"demo evaluation report not found: {DEMO_EVALUATION_REPORT}") from exc
+ except json.JSONDecodeError as exc:
+ raise HTTPException(status_code=500, detail=f"demo evaluation report is invalid JSON: {DEMO_EVALUATION_REPORT}") from exc
+
+ accuracy = report.get("accuracy") if isinstance(report, dict) else None
+ structural = report.get("structural_validation") if isinstance(report, dict) else None
+ contract = report.get("contract_validation") if isinstance(report, dict) else None
+ if not isinstance(accuracy, dict) or not isinstance(structural, dict) or not isinstance(contract, dict):
+ raise HTTPException(status_code=500, detail=f"demo evaluation report schema error: {DEMO_EVALUATION_REPORT}")
+
+ return {
+ "report_role": report.get("report_role"),
+ "source": f"examples/validation_demo/subset/{DEMO_EVALUATION_REPORT}",
+ "preset": (report.get("preset") or {}).get("name"),
+ "runtime_result": report.get("runtime_result") or {},
+ "accuracy": accuracy,
+ "structural_validation": structural,
+ "contract_validation": contract,
+ "deployment_signal": report.get("deployment_signal") or {},
+ }
+
+
+def _build_demo_job(
+ results: list[dict[str, Any]],
+ compare: dict[str, Any],
+ evaluation_report: dict[str, Any],
+) -> dict[str, Any]:
now = _utc_now_iso()
runtime_result = results[-1] if results else {}
return {
@@ -314,6 +349,7 @@ def _build_demo_job(results: list[dict[str, Any]], compare: dict[str, Any]) -> d
"runtime_result": runtime_result,
"comparison": compare,
"deployment_decision": compare["deployment_decision"],
+ "evaluation_report": evaluation_report,
"summary": compare["judgement"]["summary"],
},
"error": None,
diff --git a/inferedgelab/studio/static/app.js b/inferedgelab/studio/static/app.js
index a9f4fbe..c6cd079 100644
--- a/inferedgelab/studio/static/app.js
+++ b/inferedgelab/studio/static/app.js
@@ -28,6 +28,7 @@ let selectedJobId = null;
let compareData = null;
let activeDecision = null;
let importedResult = null;
+let demoEvaluationReport = null;
const importedResultsByJobId = {};
function createElement(tagName, className, textContent) {
@@ -365,6 +366,7 @@ async function loadDemoEvidence() {
const payload = await fetchJson("/studio/api/demo-evidence");
const results = Array.isArray(payload.results) ? payload.results : [];
importedResult = results[results.length - 1] || null;
+ demoEvaluationReport = payload.evaluation_report || null;
compareData = payload.compare || null;
selectedJobId = payload.job_id || payload.job?.job_id || selectedJobId;
selectedJob = payload.job || selectedJob;
@@ -373,6 +375,7 @@ async function loadDemoEvidence() {
setStatus("#demo-status", "Success: demo evidence loaded.", "success");
setStatus("#import-status", "Success: demo ONNX Runtime + TensorRT evidence imported.", "success");
renderImportEvidence({ result: importedResult });
+ renderDemoEvaluation(demoEvaluationReport);
renderImportedResult();
await loadJobs(selectedJobId);
await loadCompare();
@@ -385,6 +388,32 @@ async function loadDemoEvidence() {
}
}
+function renderDemoEvaluation(report) {
+ const target = document.querySelector("#demo-report-summary");
+ if (!target) {
+ return;
+ }
+ target.replaceChildren();
+
+ if (!report) {
+ return;
+ }
+
+ const metrics = report.accuracy?.metrics || {};
+ const structural = report.structural_validation || {};
+ const contract = report.contract_validation?.input_shape || {};
+ target.append(
+ evidenceItem("preset", report.preset || "yolov8_coco"),
+ evidenceItem("samples", report.runtime_result?.sample_count ?? "-"),
+ evidenceItem("mAP50", metrics.map50 === undefined ? "-" : formatNumber(metrics.map50)),
+ evidenceItem("precision", metrics.precision === undefined ? "-" : formatNumber(metrics.precision)),
+ evidenceItem("recall", metrics.recall === undefined ? "-" : formatNumber(metrics.recall)),
+ evidenceItem("structure", structural.status || "-"),
+ evidenceItem("contract", contract.status || "-"),
+ evidenceItem("report", report.source || "-"),
+ );
+}
+
function renderPipeline() {
const target = document.querySelector("#pipeline-flow");
target.replaceChildren();
@@ -446,6 +475,7 @@ function renderRunPanel() {
setState("#import-state", "idle");
setState("#jetson-state", "idle");
setState("#demo-state", "idle");
+ renderDemoEvaluation(null);
}
function resetTransientInputs() {
diff --git a/inferedgelab/studio/static/index.html b/inferedgelab/studio/static/index.html
index 495feba..3594610 100644
--- a/inferedgelab/studio/static/index.html
+++ b/inferedgelab/studio/static/index.html
@@ -137,8 +137,8 @@
}
}
-
-
+
+
@@ -266,6 +266,7 @@ Replay validation evidence
@@ -331,7 +332,7 @@ Future Work
-
-
+
+