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PyCharm Code Inspect fixes (#18392)

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Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Glenn Jocher 2024-12-25 16:24:29 +01:00 • committed by GitHub
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4
docs/en/guides/coral-edge-tpu-on-raspberry-pi.md
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@ -81,7 +81,7 @@ After installing the runtime, you need to plug in your Coral Edge TPU into a USB
sudo apt remove libedgetpu1-max
```
## Export your model to a Edge TPU compatible model
## Export to Edge TPU
To use the Edge TPU, you need to convert your model into a compatible format. It is recommended that you run export on Google Colab, x86_64 Linux machine, using the official [Ultralytics Docker container](docker-quickstart.md), or using [Ultralytics HUB](../hub/quickstart.md), since the Edge TPU compiler is not available on ARM. See the [Export Mode](../modes/export.md) for the available arguments.
@ -105,7 +105,7 @@ To use the Edge TPU, you need to convert your model into a compatible format. It
yolo export model=path/to/model.pt format=edgetpu # Export an official model or custom model
```
The exported model will be saved in the `<model_name>_saved_model/` folder with the name `<model_name>_full_integer_quant_edgetpu.tflite`. It is important that your model ends with the suffix `_edgetpu.tflite`, otherwise ultralytics doesn't know that you're using a Edge TPU model.
The exported model will be saved in the `<model_name>_saved_model/` folder with the name `<model_name>_full_integer_quant_edgetpu.tflite`. It is important that your model ends with the suffix `_edgetpu.tflite`, otherwise ultralytics doesn't know that you're using an Edge TPU model.
## Running the model

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docs/en/guides/model-deployment-options.md
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@ -280,7 +280,7 @@ 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 |
| MNN | High-performance for mobile devices. | Mobile and embedded ARM systems and X86-64 CPU | Mobile/embedded ML community | Moblile systems efficiency | High performance maintenance on Mobile Devices | On-device security advantages | ARM CPUs and GPUs optimizations |
| MNN | High-performance for mobile devices. | Mobile and embedded ARM systems and X86-64 CPU | Mobile/embedded ML community | Mobile systems efficiency | High performance maintenance on Mobile Devices | 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/guides/model-testing.md
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@ -81,7 +81,7 @@ Underfitting occurs when your model can't capture the underlying patterns in the
#### Signs of Underfitting
- **Low Training Accuracy:** If your model can't achieve high accuracy on the training set, it might be underfitting.
- **Visual Misclassification:** Consistent failure to recognize obvious features or objects suggests underfitting.
- **Visual Mis-classification:** Consistent failure to recognize obvious features or objects suggests underfitting.
### Balancing Overfitting and Underfitting

8
docs/en/guides/workouts-monitoring.md
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@ -16,7 +16,7 @@ Monitoring workouts through pose estimation with [Ultralytics YOLO11](https://gi
allowfullscreen>
</iframe>
<br>
<strong>Watch:</strong> Workouts Monitoring using Ultralytics YOLO11 | Pushups, Pullups, Ab Workouts
<strong>Watch:</strong> Workouts Monitoring using Ultralytics YOLO11 | Push-ups, Pull-ups, Ab Workouts
</p>
## Advantages of Workouts Monitoring?
@ -111,7 +111,7 @@ Monitoring workouts through pose estimation with [Ultralytics YOLO11](https://gi
### How do I monitor my workouts using Ultralytics YOLO11?
To monitor your workouts using Ultralytics YOLO11, you can utilize the pose estimation capabilities to track and analyze key body landmarks and joints in real-time. This allows you to receive instant feedback on your exercise form, count repetitions, and measure performance metrics. You can start by using the provided example code for pushups, pullups, or ab workouts as shown:
To monitor your workouts using Ultralytics YOLO11, you can utilize the pose estimation capabilities to track and analyze key body landmarks and joints in real-time. This allows you to receive instant feedback on your exercise form, count repetitions, and measure performance metrics. You can start by using the provided example code for push-ups, pull-ups, or ab workouts as shown:
```python
import cv2
@ -154,11 +154,11 @@ You can watch a [YouTube video demonstration](https://www.youtube.com/watch?v=LG
### How accurate is Ultralytics YOLO11 in detecting and tracking exercises?
Ultralytics YOLO11 is highly accurate in detecting and tracking exercises due to its state-of-the-art pose estimation capabilities. It can accurately track key body landmarks and joints, providing real-time feedback on exercise form and performance metrics. The model's pretrained weights and robust architecture ensure high [precision](https://www.ultralytics.com/glossary/precision) and reliability. For real-world examples, check out the [real-world applications](#real-world-applications) section in the documentation, which showcases pushups and pullups counting.
Ultralytics YOLO11 is highly accurate in detecting and tracking exercises due to its state-of-the-art pose estimation capabilities. It can accurately track key body landmarks and joints, providing real-time feedback on exercise form and performance metrics. The model's pretrained weights and robust architecture ensure high [precision](https://www.ultralytics.com/glossary/precision) and reliability. For real-world examples, check out the [real-world applications](#real-world-applications) section in the documentation, which showcases push-ups and pull-ups counting.
### Can I use Ultralytics YOLO11 for custom workout routines?
Yes, Ultralytics YOLO11 can be adapted for custom workout routines. The `AIGym` class supports different pose types such as "pushup", "pullup", and "abworkout." You can specify keypoints and angles to detect specific exercises. Here is an example setup:
Yes, Ultralytics YOLO11 can be adapted for custom workout routines. The `AIGym` class supports different pose types such as `pushup`, `pullup`, and `abworkout`. You can specify keypoints and angles to detect specific exercises. Here is an example setup:
```python
from ultralytics import solutions