Benchmark with custom `data.yaml` (#3858)

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

docs/modes/benchmark.md

@@ -30,22 +30,23 @@ full list of export arguments.
         from ultralytics.utils.benchmarks import benchmark
         
         # Benchmark on GPU
-        benchmark(model='yolov8n.pt', imgsz=640, half=False, device=0)
+        benchmark(model='yolov8n.pt', data='coco8.yaml', imgsz=640, half=False, device=0)
         ```
     === "CLI"
     
         ```bash
-        yolo benchmark model=yolov8n.pt imgsz=640 half=False device=0
+        yolo benchmark model=yolov8n.pt data='coco8.yaml' imgsz=640 half=False device=0
         ```
 
 ## Arguments
 
-Arguments such as `model`, `imgsz`, `half`, `device`, and `hard_fail` provide users with the flexibility to fine-tune
+Arguments such as `model`, `data`, `imgsz`, `half`, `device`, and `hard_fail` provide users with the flexibility to fine-tune
 the benchmarks to their specific needs and compare the performance of different export formats with ease.
 
 | Key         | Value   | Description                                                                |
-|-------------|---------|----------------------------------------------------------------------|
+|-------------|---------|----------------------------------------------------------------------------|
 | `model`     | `None`  | path to model file, i.e. yolov8n.pt, yolov8n.yaml                          |
+| `data`      | `None`  | path to yaml referencing the benchmarking dataset (under `val` label)      |
 | `imgsz`     | `640`   | image size as scalar or (h, w) list, i.e. (640, 480)                       |
 | `half`      | `False` | FP16 quantization                                                          |
 | `int8`      | `False` | INT8 quantization                                                          |

docs/usage/python.md

@@ -243,7 +243,7 @@ their specific use case based on their requirements for speed and accuracy.
         from ultralytics.utils.benchmarks import benchmark
         
         # Benchmark
-        benchmark(model='yolov8n.pt', imgsz=640, half=False, device=0)
+        benchmark(model='yolov8n.pt', data='coco8.yaml', imgsz=640, half=False, device=0)
         ```
 
 [Benchmark Examples](../modes/benchmark.md){ .md-button .md-button--primary}

ultralytics/utils/benchmarks.py

@@ -5,7 +5,7 @@ Benchmark a YOLO model formats for speed and accuracy
 Usage:
     from ultralytics.utils.benchmarks import ProfileModels, benchmark
     ProfileModels(['yolov8n.yaml', 'yolov8s.yaml']).profile()
-    run_benchmarks(model='yolov8n.pt', imgsz=160)
+    benchmark(model='yolov8n.pt', imgsz=160)
 
 Format                  | `format=argument`         | Model
 ---                     | ---                       | ---
@@ -44,6 +44,7 @@ from ultralytics.utils.torch_utils import select_device
 
 
 def benchmark(model=Path(SETTINGS['weights_dir']) / 'yolov8n.pt',
+              data=None,
               imgsz=160,
               half=False,
               int8=False,
@@ -55,6 +56,7 @@ def benchmark(model=Path(SETTINGS['weights_dir']) / 'yolov8n.pt',
     Args:
         model (str | Path | optional): Path to the model file or directory. Default is
             Path(SETTINGS['weights_dir']) / 'yolov8n.pt'.
+        data (str, optional): Dataset to evaluate on, inherited from TASK2DATA if not passed. Default is None.
         imgsz (int, optional): Image size for the benchmark. Default is 160.
         half (bool, optional): Use half-precision for the model if True. Default is False.
         int8 (bool, optional): Use int8-precision for the model if True. Default is False.
@@ -106,7 +108,7 @@ def benchmark(model=Path(SETTINGS['weights_dir']) / 'yolov8n.pt',
             export.predict(ROOT / 'assets/bus.jpg', imgsz=imgsz, device=device, half=half)
 
             # Validate
-            data = TASK2DATA[model.task]  # task to dataset, i.e. coco8.yaml for task=detect
+            data = data or TASK2DATA[model.task]  # task to dataset, i.e. coco8.yaml for task=detect
             key = TASK2METRIC[model.task]  # task to metric, i.e. metrics/mAP50-95(B) for task=detect
             results = export.val(data=data,
                                  batch=1,