Add custom YOLO prediction and heatmap workflow

README.md

@@ -51,9 +51,12 @@ 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. 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.
+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.
+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.
 Job APIs and the SSE log stream also expose structured progress parsed from
 YOLO, MMSeg/MMEngine, SegModel-style epoch logs, and generic tqdm percentages,
 so the queue and live log panel can show stage, epoch/iteration, and percent
@@ -139,7 +142,8 @@ 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.train_custom`, `yolo.heatmap`, `yolo.compare`,
+  `yolo.train_custom`, `yolo.predict_custom`, `yolo.heatmap`,
+  `yolo.heatmap_custom`, `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`