diff --git a/ultralytics/engine/predictor.py b/ultralytics/engine/predictor.py
index 010e2b970c..ef728b9c0b 100644
--- a/ultralytics/engine/predictor.py
+++ b/ultralytics/engine/predictor.py
@@ -44,15 +44,15 @@ from ultralytics.utils.files import increment_path
 from ultralytics.utils.torch_utils import select_device, smart_inference_mode
 
 STREAM_WARNING = """
-    WARNING ⚠️ stream/video/webcam/dir predict source will accumulate results in RAM unless `stream=True` is passed,
-    causing potential out-of-memory errors for large sources or long-running streams/videos.
-
-    Example:
-        results = model(source=..., stream=True)  # generator of Results objects
-        for r in results:
-            boxes = r.boxes  # Boxes object for bbox outputs
-            masks = r.masks  # Masks object for segment masks outputs
-            probs = r.probs  # Class probabilities for classification outputs
+WARNING ⚠️ inference results will accumulate in RAM unless `stream=True` is passed, causing potential out-of-memory
+errors for large sources or long-running streams and videos. See https://docs.ultralytics.com/modes/predict/ for help.
+
+Example:
+    results = model(source=..., stream=True)  # generator of Results objects
+    for r in results:
+        boxes = r.boxes  # Boxes object for bbox outputs
+        masks = r.masks  # Masks object for segment masks outputs
+        probs = r.probs  # Class probabilities for classification outputs
 """
 
 
diff --git a/ultralytics/models/fastsam/prompt.py b/ultralytics/models/fastsam/prompt.py
index e4657112f1..9d5ae253c9 100644
--- a/ultralytics/models/fastsam/prompt.py
+++ b/ultralytics/models/fastsam/prompt.py
@@ -68,7 +68,6 @@ class FastSAMPrompt:
         if len(contours) > 1:
             for b in contours:
                 x_t, y_t, w_t, h_t = cv2.boundingRect(b)
-                # 将多个bbox合并成一个
                 x1 = min(x1, x_t)
                 y1 = min(y1, y_t)
                 x2 = max(x2, x_t + w_t)
@@ -84,9 +83,8 @@ class FastSAMPrompt:
              mask_random_color=True,
              better_quality=True,
              retina=False,
-             withContours=True):
-        n = len(annotations)
-        pbar = TQDM(annotations, total=n)
+             with_contours=True):
+        pbar = TQDM(annotations, total=len(annotations))
         for ann in pbar:
             result_name = os.path.basename(ann.path)
             image = ann.orig_img
@@ -122,17 +120,13 @@ class FastSAMPrompt:
                     target_width=original_w,
                 )
 
-                if withContours:
+                if with_contours:
                     contour_all = []
                     temp = np.zeros((original_h, original_w, 1))
                     for i, mask in enumerate(masks):
                         mask = mask.astype(np.uint8)
                         if not retina:
-                            mask = cv2.resize(
-                                mask,
-                                (original_w, original_h),
-                                interpolation=cv2.INTER_NEAREST,
-                            )
+                            mask = cv2.resize(mask, (original_w, original_h), interpolation=cv2.INTER_NEAREST)
                         contours, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
                         contour_all.extend(iter(contours))
                     cv2.drawContours(temp, contour_all, -1, (255, 255, 255), 2)
@@ -143,17 +137,14 @@ class FastSAMPrompt:
             plt.axis('off')
             fig = plt.gcf()
 
-            try:
-                buf = fig.canvas.tostring_rgb()
-            except AttributeError:
+            # Check if the canvas has been drawn
+            if fig.canvas.get_renderer() is None:  # macOS requires this or tests fail
                 fig.canvas.draw()
-                buf = fig.canvas.tostring_rgb()
-            cols, rows = fig.canvas.get_width_height()
-            img_array = np.frombuffer(buf, dtype=np.uint8).reshape(rows, cols, 3)
 
             save_path = Path(output) / result_name
             save_path.parent.mkdir(exist_ok=True, parents=True)
-            cv2.imwrite(str(save_path), img_array)
+            image = Image.frombytes('RGB', fig.canvas.get_width_height(), fig.canvas.tostring_rgb())
+            image.save(save_path)
             plt.close()
             pbar.set_description(f'Saving {result_name} to {save_path}')
 
@@ -289,7 +280,7 @@ class FastSAMPrompt:
             if h != target_height or w != target_width:
                 points = [[int(point[0] * w / target_width), int(point[1] * h / target_height)] for point in points]
             onemask = np.zeros((h, w))
-            for i, annotation in enumerate(masks):
+            for annotation in masks:
                 mask = annotation['segmentation'] if isinstance(annotation, dict) else annotation
                 for i, point in enumerate(points):
                     if mask[point[1], point[0]] == 1 and pointlabel[i] == 1:
diff --git a/ultralytics/utils/benchmarks.py b/ultralytics/utils/benchmarks.py
index e92a9ae783..ad1bcf30e3 100644
--- a/ultralytics/utils/benchmarks.py
+++ b/ultralytics/utils/benchmarks.py
@@ -263,7 +263,7 @@ class ProfileModels:
             data = clipped_data
         return data
 
-    def profile_tensorrt_model(self, engine_file: str, eps: float = 1e-7):
+    def profile_tensorrt_model(self, engine_file: str, eps: float = 1e-3):
         if not self.trt or not Path(engine_file).is_file():
             return 0.0, 0.0
 
@@ -291,7 +291,7 @@ class ProfileModels:
         run_times = self.iterative_sigma_clipping(np.array(run_times), sigma=2, max_iters=3)  # sigma clipping
         return np.mean(run_times), np.std(run_times)
 
-    def profile_onnx_model(self, onnx_file: str, eps: float = 1e-7):
+    def profile_onnx_model(self, onnx_file: str, eps: float = 1e-3):
         check_requirements('onnxruntime')
         import onnxruntime as ort