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

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: fang_chenfang <1217690899@qq.com>
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Glenn Jocher 2024-03-01 00:53:17 +01:00 • committed by GitHub
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6
docs/en/guides/model-deployment-options.md
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@ -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.

2
docs/en/modes/benchmark.md
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@ -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.

2
docs/en/modes/export.md
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@ -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` |

2
docs/en/modes/predict.md
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@ -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()

2
docs/en/tasks/classify.md
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@ -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.

2
docs/en/tasks/detect.md
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@ -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.

2
docs/en/tasks/obb.md
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@ -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.

2
docs/en/tasks/pose.md
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@ -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.

2
docs/en/tasks/segment.md
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@ -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.

2
docs/en/usage/cli.md
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@ -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