Show dataset YOLO training curves

README.md

@@ -55,7 +55,7 @@ for the `yolo.train_custom` task. The selected upload dataset also exposes
 direct YOLO custom train, predict, and heatmap actions; custom outputs are
 written under `var/custom_yolo_runs` and are scanned by the results dashboard.
 When a dataset is selected, the dataset panel shows its custom YOLO `best.pt`,
-prediction previews, heatmap previews, and detected training curves.
+prediction previews, heatmap previews, and inline training curve previews.
 Segmentation previews, YOLO heatmaps, and loss/metric artifacts are grouped on
 the results dashboard, and YOLO-style `results.csv` files are parsed into
 lightweight training curves.

backend/app/agents/evaluation_agent.py

@@ -44,7 +44,11 @@ def evaluate_project() -> dict:
         "upload_ui": "uploadDatasetFiles" in frontend_text and "labels" in frontend_text and "masks" in frontend_text,
         "dataset_quality_ui": "DatasetQuality" in frontend_text and "generateSelectedYoloYaml" in frontend_text,
         "uploaded_yolo_workflow_ui": "startSelectedYoloTrain" in frontend_text and "startSelectedYoloPredict" in frontend_text and "startSelectedYoloHeatmap" in frontend_text,
-        "dataset_yolo_outputs_ui": "DatasetYoloOutputs" in frontend_text and "selectedYoloOutputs" in frontend_text and "BEST.PT READY" in frontend_text,
+        "dataset_yolo_outputs_ui": "DatasetYoloOutputs" in frontend_text
+        and "selectedYoloOutputs" in frontend_text
+        and "BEST.PT READY" in frontend_text
+        and "datasetOutputCurve" in frontend_text
+        and "MiniCurvePlot" in frontend_text,
         "loss_result_ui": "loss" in frontend_text.lower() and "heatmap" in frontend_text.lower() and "CurvePanel" in frontend_text,
         "job_progress_ui": "JobProgressBar" in frontend_text and "progressTrack" in frontend_text,
         "runtime_readiness_ui": "runtimeReadiness" in frontend_text and "环境就绪" in frontend_text,

backend/app/main.py

@@ -216,8 +216,8 @@ def api_results() -> list[dict]:
 
 
 @app.get("/api/results/curves")
-def api_result_curves() -> list[dict]:
+def api_result_curves(limit: int = 100) -> list[dict]:
-    return scan_training_curves()
+    return scan_training_curves(limit=limit)
 
 
 @app.get("/api/artifacts/{artifact_path:path}")

frontend/src/main.tsx

@@ -1289,6 +1289,8 @@ function DatasetQuality({ validation }: { validation: DatasetValidation }) {
 
 function DatasetYoloOutputs({ dataset, outputs }: { dataset: UploadedDataset; outputs: DatasetYoloOutputsPayload }) {
   const previewItems = [...outputs.heatmaps.slice(0, 3), ...outputs.predictions.slice(0, 3)].slice(0, 6);
+  const primaryCurve = outputs.curves[0];
+  const curveSeries = primaryCurve?.series.slice(0, 4) ?? [];
   return (
     <div className="datasetOutputBox">
       <div className="qualityHead">
@@ -1315,6 +1317,15 @@ function DatasetYoloOutputs({ dataset, outputs }: { dataset: UploadedDataset; ou
           </a>
         ))}
       </div>
+      {primaryCurve && !!curveSeries.length && (
+        <div className="datasetOutputCurve">
+          <div>
+            <strong>{primaryCurve.name}</strong>
+            <span>{primaryCurve.row_count} epochs · {primaryCurve.family}</span>
+          </div>
+          <MiniCurvePlot series={curveSeries} />
+        </div>
+      )}
       {!!previewItems.length && (
         <div className="datasetOutputPreview">
           {previewItems.map((item) => (

frontend/src/styles.css

@@ -853,6 +853,48 @@ textarea {
   white-space: nowrap;
 }
 
+.datasetOutputCurve {
+  display: grid;
+  gap: 8px;
+  padding: 8px;
+  border-radius: 6px;
+  border: 1px solid var(--line);
+  background: #101310;
+}
+
+.datasetOutputCurve > div {
+  min-width: 0;
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
+  gap: 8px;
+}
+
+.datasetOutputCurve strong,
+.datasetOutputCurve span {
+  min-width: 0;
+  overflow: hidden;
+  text-overflow: ellipsis;
+  white-space: nowrap;
+}
+
+.datasetOutputCurve strong {
+  flex: 1 1 auto;
+}
+
+.datasetOutputCurve span {
+  flex: 0 1 auto;
+  color: var(--muted);
+  font-size: 11px;
+}
+
+.datasetOutputCurve .curveSvg,
+.datasetOutputCurve .curveEmpty {
+  min-height: 96px;
+  aspect-ratio: 2.6 / 1;
+  border-radius: 6px;
+}
+
 .datasetOutputPreview {
   display: grid;
   grid-template-columns: repeat(3, minmax(0, 1fr));