Add dataset QA and custom YOLO training flow

README.md

@@ -45,9 +45,11 @@ The web UI includes a dataset bench for creating upload workspaces, uploading
 images/labels/masks, and jumping into the existing rename, PNG conversion,
 resize, pair-check, label rebuild, transparent overlay, stitch, and video-frame
 jobs. Selecting an uploaded dataset fills task JSON with its images, labels,
-and masks directories. 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.
+and masks directories. The dataset panel validates image/label/mask pairing,
+checks YOLO txt labels and mask dimensions, and can generate a `dataset.yaml`
+for the `yolo.train_custom` task. 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.
 
 The coverage panel calls `GET /api/coverage` and verifies that the user-facing
 scripts from the existing `Seg/` workspace are mapped to web jobs. MMSeg
@@ -119,7 +121,8 @@ The backend exposes all current Seg capabilities as job types. Examples:
   `segmodel.batch_predict`, `segmodel.flops`, `segmodel.params_flops`,
   `segmodel.benchmark`, `segmodel.raw_mask_check`
 - `yolo.train`, `yolo.batch_train`, `yolo.predict`, `yolo.batch_predict`,
-  `yolo.heatmap`, `yolo.compare`, `yolo.raw_mask_check`, `yolo.video_visible`
+  `yolo.train_custom`, `yolo.heatmap`, `yolo.compare`,
+  `yolo.raw_mask_check`, `yolo.video_visible`
 - `mmseg.generate_data`, `mmseg.generate_alg`, `mmseg.train`,
   `mmseg.metrics`, `mmseg.flops_fps`, `mmseg.draw`, `mmseg.extract_loss_miou`
 - `visual.train`, `visual.inference`, `visual.fps`,