feat: Upload model weights for CABiNet; Update README with flops and …

README.md

@@ -47,17 +47,19 @@ _Columns: Input images, State-of-the-art predictions, CABiNet predictions (white
 
 CABiNet and Ultralytics YOLO26 (`semantic` task, dense per-pixel — not `-seg`) are trained and evaluated under one shared UAVid pipeline (`images/`+`masks/` format, 8 classes incl. Clutter).
 
-| Model                       | mIoU (%) | Params (M) | FLOPs (B) | HF Weights                                                                    |
-| --------------------------- | -------- | ---------- | --------- | ----------------------------------------------------------------------------- |
-| CABiNet (MobileNetV3-Large) | TBD¹     | TBD        | TBD       | [HF Model](https://huggingface.co/dronefreak/cabinet-mobilenetv3-small-uavid) |
-| CABiNet (MobileNetV3-Small) | TBD²     | TBD        | TBD       | [HF Model](https://huggingface.co/dronefreak/cabinet-mobilenetv3-large-uavid) |
-| YOLO26n-sem                 | 63.58    | TBD        | TBD       | [HF Model](https://huggingface.co/dronefreak/uavid-yolo26n-sem)               |
-| YOLO26s-sem                 | 66.88    | TBD        | TBD       | [HF Model](https://huggingface.co/dronefreak/uavid-yolo26s-sem)               |
-| YOLO26m-sem                 | 67.66    | TBD        | TBD       | [HF Model](https://huggingface.co/dronefreak/uavid-yolo26m-sem)               |
-| YOLO26l-sem                 | 67.20    | TBD        | TBD       | [HF Model](https://huggingface.co/dronefreak/uavid-yolo26l-sem)               |
-| YOLO26x-sem                 | TBD³     | TBD        | TBD       | [HF Model](https://huggingface.co/dronefreak/uavid-yolo26x-sem)               |
-
-¹ Trained under the refactored pipeline but not yet re-evaluated. ² Not yet trained. ³ Not yet trained.
+All numbers below are test-split mIoU (not val — see [`train_yolo.py`](src/scripts/train_yolo.py)'s `validation_config.split`). Params/FLOPs are architecture-only, measured at 1024×1024 via `thop` (CABiNet) / Ultralytics' built-in profiler (YOLO26, also `thop`-backed — both report FLOPs as 2× MACs, so the two families are directly comparable).
+
+| Model                       | mIoU (%) | Params (M) | FLOPs (GFLOPs) | HF Weights                                                                    |
+| --------------------------- | -------- | ---------- | -------------- | ----------------------------------------------------------------------------- |
+| CABiNet (MobileNetV3-Large) | 68.60    | 9.17       | 54.8           | [HF Model](https://huggingface.co/dronefreak/cabinet-mobilenetv3-large-uavid) |
+| CABiNet (MobileNetV3-Small) | 66.84    | 5.36       | 44.1           | [HF Model](https://huggingface.co/dronefreak/cabinet-mobilenetv3-small-uavid) |
+| YOLO26x-sem                 | 64.41    | 40.16      | 430.9          | [HF Model](https://huggingface.co/dronefreak/uavid-yolo26x-sem)               |
+| YOLO26l-sem                 | 63.28    | 17.87      | 192.4          | [HF Model](https://huggingface.co/dronefreak/uavid-yolo26l-sem)               |
+| YOLO26m-sem                 | 61.98    | 14.32      | 152.3          | [HF Model](https://huggingface.co/dronefreak/uavid-yolo26m-sem)               |
+| YOLO26s-sem                 | 61.69    | 6.50       | 44.4           | [HF Model](https://huggingface.co/dronefreak/uavid-yolo26s-sem)               |
+| YOLO26n-sem                 | 58.17    | 1.63       | 11.4           | [HF Model](https://huggingface.co/dronefreak/uavid-yolo26n-sem)               |
+
+CABiNet (both backbones) outperforms **every** YOLO26-sem variant on UAVid, including the largest (YOLO26x) — and does so at a fraction of the compute: CABiNet-Large (54.8 GFLOPs) beats YOLO26x (430.9 GFLOPs, ~8× more) and YOLO26m (152.3 GFLOPs, ~3× more) on both mIoU and FLOPs simultaneously. CABiNet-Small also improves substantially over the numbers originally reported in the [CABiNet paper](#citation) on this dataset.
 
 ## Installation
 
@@ -422,7 +424,7 @@ python src/scripts/visualize.py
 
 ### Performance Profiling
 
-Benchmark inference timing and memory usage (`profile_model_flops` currently measures `torch.profiler` CPU/CUDA time, not analytic FLOP counts — real FLOPs for the [UAVid Model Zoo](#uavid-model-zoo) table are still TBD):
+Benchmark inference timing and memory usage (`profile_model_flops` measures `torch.profiler` CPU/CUDA time, not analytic FLOP counts — the analytic Params/FLOPs in the [UAVid Model Zoo](#uavid-model-zoo) table above were computed separately via `thop`):
 
 ```python
 from src.utils.profiler import PerformanceProfiler

configs/train_yolo.yaml

@@ -115,6 +115,7 @@ validation_config:
   save_json: true
   augment: false # TTA: set true on CLI for augmented eval
   weights: ~ # null → auto-inferred from experiments_path/model.run_name
+  split: val # 'val' or 'test' — override: validation_config.split=test
 
 # ── Runtime ──────────────────────────────────────────────────────────────────
 runtime:

src/scripts/train_yolo.py

@@ -1,6 +1,7 @@
 #!/usr/bin/python
 # -*- encoding: utf-8 -*-
 
+import json
 import logging
 from pathlib import Path
 
@@ -11,6 +12,21 @@
 
 logger = logging.getLogger(__name__)
 
+# UAVid classes in trainId order (0-7) — matches configs/UAVid_info.json and
+# hf_modelcards/generate_hf_model_zoo.py's UAVID_CLASSES. Used only to label
+# mode=val's per-class IoU printout in a form directly pastable into a
+# hf_modelcards/model_metrics/<model>/metrics.json.
+UAVID_CLASS_NAMES = [
+    "Clutter",
+    "Building",
+    "Road",
+    "Static Car",
+    "Tree",
+    "Vegetation",
+    "Human",
+    "Moving Car",
+]
+
 # Repo root: src/scripts/train_yolo.py -> src/scripts -> src -> <repo root>
 REPO_ROOT = Path(__file__).resolve().parents[2]
 
@@ -164,7 +180,7 @@ def _build_val_kwargs(cfg: DictConfig, dataset_path: Path, weights: str) -> dict
         "imgsz": int(cfg.training_config.imgsz),
         "batch": int(vc.get("batch_size", 1)),
         "device": cfg.runtime.get("device", 0),
-        "split": "val",
+        "split": str(vc.get("split", "val")),
         "save_json": bool(vc.get("save_json", True)),
         "augment": bool(vc.get("augment", False)),
         "plots": True,
@@ -239,12 +255,34 @@ def main(cfg: DictConfig) -> None:
         console.print(f"Validating with weights: {weights_path}", style="info")
         model = YOLO(str(weights_path))
         val_kwargs = _build_val_kwargs(cfg, dataset_path, str(weights_path))
+        split = val_kwargs["split"]
 
         results = model.val(**val_kwargs)
         if results is not None:
+            console.print(f"Split: {split}", style="info")
             console.print(f"mIoU: {results.miou:.4f}", style="info")
             console.print(f"Pixel accuracy: {results.pixel_accuracy:.4f}", style="info")
 
+            per_class_iou = {
+                name: round(float(iou), 4)
+                for name, iou in zip(UAVID_CLASS_NAMES, results.per_class_iou)
+            }
+            console.print("Per-class IoU:", style="info")
+            for name, iou in per_class_iou.items():
+                console.print(f"  {name}: {iou:.4f}", style="info")
+
+            metrics_snippet = {
+                "eval_split": split,
+                "miou": round(float(results.miou), 4),
+                "pixel_acc": round(float(results.pixel_accuracy), 4),
+                "per_class_iou": per_class_iou,
+            }
+            console.print(
+                "\nPaste into hf_modelcards/model_metrics/<model>/metrics.json:",
+                style="info",
+            )
+            console.print(json.dumps(metrics_snippet, indent=2), style="info")
+
     else:
         raise ValueError(
             f"Unknown mode '{mode}'. Choose 'train' or 'val'.\n"