Fix undefined ‘im_array’ bug in predict.md (#8565)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: fang_chenfang <1217690899@qq.com>

docs/en/guides/model-deployment-options.md

@@ -240,9 +240,9 @@ PaddlePaddle is an open-source deep learning framework developed by Baidu. It is
 
 - **Hardware Acceleration**: Supports various hardware accelerations, including Baidu's own Kunlun chips.
 
-#### ncnn
+#### NCNN
 
-ncnn is a high-performance neural network inference framework optimized for the mobile platform. It stands out for its lightweight nature and efficiency, making it particularly well-suited for mobile and embedded devices where resources are limited.
+NCNN is a high-performance neural network inference framework optimized for the mobile platform. It stands out for its lightweight nature and efficiency, making it particularly well-suited for mobile and embedded devices where resources are limited.
 
 - **Performance Benchmarks**: Highly optimized for mobile platforms, offering efficient inference on ARM-based devices.
 
@@ -276,7 +276,7 @@ The following table provides a snapshot of the various deployment options availa
 | TF Edge TPU       | Optimized for Google's Edge TPU hardware        | Exclusive to Edge TPU devices                  | Growing with Google and third-party resources | IoT devices requiring real-time processing | Improvements for new Edge TPU hardware      | Google's robust IoT security                      | Custom-designed for Google Coral   |
 | TF.js             | Reasonable in-browser performance               | High with web technologies                     | Web and Node.js developers support            | Interactive web applications               | TensorFlow team and community contributions | Web platform security model                       | Enhanced with WebGL and other APIs |
 | PaddlePaddle      | Competitive, easy to use and scalable           | Baidu ecosystem, wide application support      | Rapidly growing, especially in China          | Chinese market and language processing     | Focus on Chinese AI applications            | Emphasizes data privacy and security              | Including Baidu's Kunlun chips     |
-| ncnn              | Optimized for mobile ARM-based devices          | Mobile and embedded ARM systems                | Niche but active mobile/embedded ML community | Android and ARM systems efficiency         | High performance maintenance on ARM         | On-device security advantages                     | ARM CPUs and GPUs optimizations    |
+| NCNN              | Optimized for mobile ARM-based devices          | Mobile and embedded ARM systems                | Niche but active mobile/embedded ML community | Android and ARM systems efficiency         | High performance maintenance on ARM         | On-device security advantages                     | ARM CPUs and GPUs optimizations    |
 
 This comparative analysis gives you a high-level overview. For deployment, it's essential to consider the specific requirements and constraints of your project, and consult the detailed documentation and resources available for each option.
 

docs/en/modes/benchmark.md

@@ -101,6 +101,6 @@ Benchmarks will attempt to run automatically on all possible export formats belo
 | [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/)         | `edgetpu`         | `yolov8n_edgetpu.tflite`  | ✅        | `imgsz`                                             |
 | [TF.js](https://www.tensorflow.org/js)                             | `tfjs`            | `yolov8n_web_model/`      | ✅        | `imgsz`, `half`, `int8`                             |
 | [PaddlePaddle](https://github.com/PaddlePaddle)                    | `paddle`          | `yolov8n_paddle_model/`   | ✅        | `imgsz`                                             |
-| [ncnn](https://github.com/Tencent/ncnn)                            | `ncnn`            | `yolov8n_ncnn_model/`     | ✅        | `imgsz`, `half`                                     |
+| [NCNN](https://github.com/Tencent/ncnn)                            | `ncnn`            | `yolov8n_ncnn_model/`     | ✅        | `imgsz`, `half`                                     |
 
 See full `export` details in the [Export](https://docs.ultralytics.com/modes/export/) page.

docs/en/modes/export.md

@@ -108,4 +108,4 @@ Available YOLOv8 export formats are in the table below. You can export to any fo
 | [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/)         | `edgetpu`         | `yolov8n_edgetpu.tflite`  | ✅        | `imgsz`                                             |
 | [TF.js](https://www.tensorflow.org/js)                             | `tfjs`            | `yolov8n_web_model/`      | ✅        | `imgsz`, `half`, `int8`                             |
 | [PaddlePaddle](https://github.com/PaddlePaddle)                    | `paddle`          | `yolov8n_paddle_model/`   | ✅        | `imgsz`                                             |
-| [ncnn](https://github.com/Tencent/ncnn)                            | `ncnn`            | `yolov8n_ncnn_model/`     | ✅        | `imgsz`, `half`                                     |
+| [NCNN](https://github.com/Tencent/ncnn)                            | `ncnn`            | `yolov8n_ncnn_model/`     | ✅        | `imgsz`, `half`                                     |

docs/en/modes/predict.md

@@ -683,7 +683,7 @@ The `plot()` method in `Results` objects facilitates visualization of prediction
     for i, r in enumerate(results):
         # Plot results image
         im_bgr = r.plot()  # BGR-order numpy array
-        im_rgb = Image.fromarray(im_array[..., ::-1])  # RGB-order PIL image
+        im_rgb = Image.fromarray(im_bgr[..., ::-1])  # RGB-order PIL image
         
         # Show results to screen (in supported environments)
         r.show()

docs/en/tasks/classify.md

@@ -176,6 +176,6 @@ Available YOLOv8-cls export formats are in the table below. You can predict or v
 | [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/)         | `edgetpu`         | `yolov8n-cls_edgetpu.tflite`  | ✅        | `imgsz`                                             |
 | [TF.js](https://www.tensorflow.org/js)                             | `tfjs`            | `yolov8n-cls_web_model/`      | ✅        | `imgsz`, `half`, `int8`                             |
 | [PaddlePaddle](https://github.com/PaddlePaddle)                    | `paddle`          | `yolov8n-cls_paddle_model/`   | ✅        | `imgsz`                                             |
-| [ncnn](https://github.com/Tencent/ncnn)                            | `ncnn`            | `yolov8n-cls_ncnn_model/`     | ✅        | `imgsz`, `half`                                     |
+| [NCNN](https://github.com/Tencent/ncnn)                            | `ncnn`            | `yolov8n-cls_ncnn_model/`     | ✅        | `imgsz`, `half`                                     |
 
 See full `export` details in the [Export](https://docs.ultralytics.com/modes/export/) page.

docs/en/tasks/detect.md

@@ -177,6 +177,6 @@ Available YOLOv8 export formats are in the table below. You can predict or valid
 | [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/)         | `edgetpu`         | `yolov8n_edgetpu.tflite`  | ✅        | `imgsz`                                             |
 | [TF.js](https://www.tensorflow.org/js)                             | `tfjs`            | `yolov8n_web_model/`      | ✅        | `imgsz`, `half`, `int8`                             |
 | [PaddlePaddle](https://github.com/PaddlePaddle)                    | `paddle`          | `yolov8n_paddle_model/`   | ✅        | `imgsz`                                             |
-| [ncnn](https://github.com/Tencent/ncnn)                            | `ncnn`            | `yolov8n_ncnn_model/`     | ✅        | `imgsz`, `half`                                     |
+| [NCNN](https://github.com/Tencent/ncnn)                            | `ncnn`            | `yolov8n_ncnn_model/`     | ✅        | `imgsz`, `half`                                     |
 
 See full `export` details in the [Export](https://docs.ultralytics.com/modes/export/) page.

docs/en/tasks/obb.md

@@ -186,6 +186,6 @@ Available YOLOv8-obb export formats are in the table below. You can predict or v
 | [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/)         | `edgetpu`         | `yolov8n-obb_edgetpu.tflite`  | ✅        | `imgsz`                                             |
 | [TF.js](https://www.tensorflow.org/js)                             | `tfjs`            | `yolov8n-obb_web_model/`      | ✅        | `imgsz`, `half`, `int8`                             |
 | [PaddlePaddle](https://github.com/PaddlePaddle)                    | `paddle`          | `yolov8n-obb_paddle_model/`   | ✅        | `imgsz`                                             |
-| [ncnn](https://github.com/Tencent/ncnn)                            | `ncnn`            | `yolov8n-obb_ncnn_model/`     | ✅        | `imgsz`, `half`                                     |
+| [NCNN](https://github.com/Tencent/ncnn)                            | `ncnn`            | `yolov8n-obb_ncnn_model/`     | ✅        | `imgsz`, `half`                                     |
 
 See full `export` details in the [Export](https://docs.ultralytics.com/modes/export/) page.

docs/en/tasks/pose.md

@@ -180,6 +180,6 @@ Available YOLOv8-pose export formats are in the table below. You can predict or
 | [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/)         | `edgetpu`         | `yolov8n-pose_edgetpu.tflite`  | ✅        | `imgsz`                                             |
 | [TF.js](https://www.tensorflow.org/js)                             | `tfjs`            | `yolov8n-pose_web_model/`      | ✅        | `imgsz`, `half`, `int8`                             |
 | [PaddlePaddle](https://github.com/PaddlePaddle)                    | `paddle`          | `yolov8n-pose_paddle_model/`   | ✅        | `imgsz`                                             |
-| [ncnn](https://github.com/Tencent/ncnn)                            | `ncnn`            | `yolov8n-pose_ncnn_model/`     | ✅        | `imgsz`, `half`                                     |
+| [NCNN](https://github.com/Tencent/ncnn)                            | `ncnn`            | `yolov8n-pose_ncnn_model/`     | ✅        | `imgsz`, `half`                                     |
 
 See full `export` details in the [Export](https://docs.ultralytics.com/modes/export/) page.

docs/en/tasks/segment.md

@@ -182,6 +182,6 @@ Available YOLOv8-seg export formats are in the table below. You can predict or v
 | [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/)         | `edgetpu`         | `yolov8n-seg_edgetpu.tflite`  | ✅        | `imgsz`                                             |
 | [TF.js](https://www.tensorflow.org/js)                             | `tfjs`            | `yolov8n-seg_web_model/`      | ✅        | `imgsz`, `half`, `int8`                             |
 | [PaddlePaddle](https://github.com/PaddlePaddle)                    | `paddle`          | `yolov8n-seg_paddle_model/`   | ✅        | `imgsz`                                             |
-| [ncnn](https://github.com/Tencent/ncnn)                            | `ncnn`            | `yolov8n-seg_ncnn_model/`     | ✅        | `imgsz`, `half`                                     |
+| [NCNN](https://github.com/Tencent/ncnn)                            | `ncnn`            | `yolov8n-seg_ncnn_model/`     | ✅        | `imgsz`, `half`                                     |
 
 See full `export` details in the [Export](https://docs.ultralytics.com/modes/export/) page.

docs/en/usage/cli.md

@@ -184,7 +184,7 @@ Available YOLOv8 export formats are in the table below. You can export to any fo
 | [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/)         | `edgetpu`         | `yolov8n_edgetpu.tflite`  | ✅        | `imgsz`                                             |
 | [TF.js](https://www.tensorflow.org/js)                             | `tfjs`            | `yolov8n_web_model/`      | ✅        | `imgsz`, `half`, `int8`                             |
 | [PaddlePaddle](https://github.com/PaddlePaddle)                    | `paddle`          | `yolov8n_paddle_model/`   | ✅        | `imgsz`                                             |
-| [ncnn](https://github.com/Tencent/ncnn)                            | `ncnn`            | `yolov8n_ncnn_model/`     | ✅        | `imgsz`, `half`                                     |
+| [NCNN](https://github.com/Tencent/ncnn)                            | `ncnn`            | `yolov8n_ncnn_model/`     | ✅        | `imgsz`, `half`                                     |
 
 ## Overriding default arguments
 

ultralytics/engine/exporter.py

@@ -16,7 +16,7 @@ TensorFlow Lite         | `tflite`                  | yolov8n.tflite
 TensorFlow Edge TPU     | `edgetpu`                 | yolov8n_edgetpu.tflite
 TensorFlow.js           | `tfjs`                    | yolov8n_web_model/
 PaddlePaddle            | `paddle`                  | yolov8n_paddle_model/
-ncnn                    | `ncnn`                    | yolov8n_ncnn_model/
+NCNN                    | `ncnn`                    | yolov8n_ncnn_model/
 
 Requirements:
     $ pip install "ultralytics[export]"
@@ -293,7 +293,7 @@ class Exporter:
                 f[9], _ = self.export_tfjs()
         if paddle:  # PaddlePaddle
             f[10], _ = self.export_paddle()
-        if ncnn:  # ncnn
+        if ncnn:  # NCNN
             f[11], _ = self.export_ncnn()
 
         # Finish
@@ -496,14 +496,14 @@ class Exporter:
         return f, None
 
     @try_export
-    def export_ncnn(self, prefix=colorstr("ncnn:")):
+    def export_ncnn(self, prefix=colorstr("NCNN:")):
         """
-        YOLOv8 ncnn export using PNNX https://github.com/pnnx/pnnx.
+        YOLOv8 NCNN export using PNNX https://github.com/pnnx/pnnx.
         """
         check_requirements("ncnn")
         import ncnn  # noqa
 
-        LOGGER.info(f"\n{prefix} starting export with ncnn {ncnn.__version__}...")
+        LOGGER.info(f"\n{prefix} starting export with NCNN {ncnn.__version__}...")
         f = Path(str(self.file).replace(self.file.suffix, f"_ncnn_model{os.sep}"))
         f_ts = self.file.with_suffix(".torchscript")
 

ultralytics/nn/autobackend.py

@@ -72,7 +72,7 @@ class AutoBackend(nn.Module):
             | TensorFlow Lite       | *.tflite         |
             | TensorFlow Edge TPU   | *_edgetpu.tflite |
             | PaddlePaddle          | *_paddle_model   |
-            | ncnn                  | *_ncnn_model     |
+            | NCNN                  | *_ncnn_model     |
 
     This class offers dynamic backend switching capabilities based on the input model format, making it easier to deploy
     models across various platforms.
@@ -304,9 +304,9 @@ class AutoBackend(nn.Module):
             input_handle = predictor.get_input_handle(predictor.get_input_names()[0])
             output_names = predictor.get_output_names()
             metadata = w.parents[1] / "metadata.yaml"
-        elif ncnn:  # ncnn
+        elif ncnn:  # NCNN
-            LOGGER.info(f"Loading {w} for ncnn inference...")
+            LOGGER.info(f"Loading {w} for NCNN inference...")
-            check_requirements("git+https://github.com/Tencent/ncnn.git" if ARM64 else "ncnn")  # requires ncnn
+            check_requirements("git+https://github.com/Tencent/ncnn.git" if ARM64 else "ncnn")  # requires NCNN
             import ncnn as pyncnn
 
             net = pyncnn.Net()
@@ -431,7 +431,7 @@ class AutoBackend(nn.Module):
             self.input_handle.copy_from_cpu(im)
             self.predictor.run()
             y = [self.predictor.get_output_handle(x).copy_to_cpu() for x in self.output_names]
-        elif self.ncnn:  # ncnn
+        elif self.ncnn:  # NCNN
             mat_in = self.pyncnn.Mat(im[0].cpu().numpy())
             ex = self.net.create_extractor()
             input_names, output_names = self.net.input_names(), self.net.output_names()

ultralytics/utils/benchmarks.py

@@ -21,7 +21,7 @@ TensorFlow Lite         | `tflite`                  | yolov8n.tflite
 TensorFlow Edge TPU     | `edgetpu`                 | yolov8n_edgetpu.tflite
 TensorFlow.js           | `tfjs`                    | yolov8n_web_model/
 PaddlePaddle            | `paddle`                  | yolov8n_paddle_model/
-ncnn                    | `ncnn`                    | yolov8n_ncnn_model/
+NCNN                    | `ncnn`                    | yolov8n_ncnn_model/
 """
 
 import glob