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ultralytics 8.0.97 confusion matrix, windows, docs updates (#2511)

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Co-authored-by: Yonghye Kwon <developer.0hye@gmail.com>
Co-authored-by: Dowon <ks2515@naver.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
2023-05-09 21:20:34 +02:00
committed by GitHub
parent 6ee3a9a74b
commit d1107ca4cb
138 changed files with 744 additions and 351 deletions
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1
docs/modes/benchmark.md
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@ -1,5 +1,6 @@
---
comments: true
description: Benchmark mode compares speed and accuracy of various YOLOv8 export formats like ONNX or OpenVINO. Optimize formats for speed or accuracy.
---
<img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png">

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docs/modes/export.md
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@ -1,5 +1,6 @@
---
comments: true
description: 'Export mode: Create a deployment-ready YOLOv8 model by converting it to various formats. Export to ONNX or OpenVINO for up to 3x CPU speedup.'
---
<img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png">
@ -82,4 +83,4 @@ i.e. `format='onnx'` or `format='engine'`.
| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8` |
| [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` |
| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n_paddle_model/` | ✅ | `imgsz` |
| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n_paddle_model/` | ✅ | `imgsz` |

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docs/modes/index.md
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@ -1,5 +1,6 @@
---
comments: true
description: Use Ultralytics YOLOv8 Modes (Train, Val, Predict, Export, Track, Benchmark) to train, validate, predict, track, export or benchmark.
---
# Ultralytics YOLOv8 Modes
@ -63,4 +64,4 @@ or `accuracy_top5` metrics (for classification), and the inference time in milli
formats like ONNX, OpenVINO, TensorRT and others. This information can help users choose the optimal export format for
their specific use case based on their requirements for speed and accuracy.
[Benchmark Examples](benchmark.md){ .md-button .md-button--primary}
[Benchmark Examples](benchmark.md){ .md-button .md-button--primary}

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docs/modes/predict.md
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@ -1,5 +1,6 @@
---
comments: true
description: Get started with YOLOv8 Predict mode and input sources. Accepts various input sources such as images, videos, and directories.
---
<img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png">
@ -58,10 +59,11 @@ whether each source can be used in streaming mode with `stream=True` ✅ and an
| YouTube ✅ | `'https://youtu.be/Zgi9g1ksQHc'` | `str` | |
| stream ✅ | `'rtsp://example.com/media.mp4'` | `str` | RTSP, RTMP, HTTP |
## Arguments
`model.predict` accepts multiple arguments that control the prediction operation. These arguments can be passed directly to `model.predict`:
!!! example
```
model.predict(source, save=True, imgsz=320, conf=0.5)
```
@ -220,6 +222,7 @@ masks, classification logits, etc.) found in the results object
res_plotted = res[0].plot()
cv2.imshow("result", res_plotted)
```
| Argument | Description |
|-------------------------------|----------------------------------------------------------------------------------------|
| `conf (bool)` | Whether to plot the detection confidence score. |
@ -234,7 +237,6 @@ masks, classification logits, etc.) found in the results object
| `masks (bool)` | Whether to plot the masks. |
| `probs (bool)` | Whether to plot classification probability. |
## Streaming Source `for`-loop
Here's a Python script using OpenCV (cv2) and YOLOv8 to run inference on video frames. This script assumes you have already installed the necessary packages (opencv-python and ultralytics).
@ -277,4 +279,4 @@ Here's a Python script using OpenCV (cv2) and YOLOv8 to run inference on video f
# Release the video capture object and close the display window
cap.release()
cv2.destroyAllWindows()
```
```

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docs/modes/val.md
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@ -1,5 +1,6 @@
---
comments: true
description: Validate and improve YOLOv8n model accuracy on COCO128 and other datasets using hyperparameter & configuration tuning, in Val mode.
---
<img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png">
@ -87,4 +88,4 @@ i.e. `format='onnx'` or `format='engine'`.
| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8` |
| [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` |
| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n_paddle_model/` | ✅ | `imgsz` |
| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n_paddle_model/` | ✅ | `imgsz` |