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ultralytics 8.0.67 Pose speeds, Comet and ClearML updates (#1871)

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Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Victor Sonck <victor.sonck@gmail.com>
Co-authored-by: Danny Kim <dh031200@gmail.com>
This commit is contained in:
Glenn Jocher
2023-04-06 19:07:10 +02:00
committed by GitHub
parent 1cb92d7f42
commit 2725545090
28 changed files with 547 additions and 146 deletions
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20
docs/tasks/pose.md
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@ -25,12 +25,12 @@ Ultralytics [release](https://github.com/ultralytics/assets/releases) on first u
| Model | size<br><sup>(pixels) | mAP<sup>box<br>50-95 | mAP<sup>pose<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
|------------------------------------------------------------------------------------------------------|-----------------------|----------------------|-----------------------|--------------------------------|-------------------------------------|--------------------|-------------------|
| [YOLOv8n-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-pose.pt) | 640 | - | 49.7 | - | - | 3.3 | 9.2 |
| [YOLOv8s-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-pose.pt) | 640 | - | 59.2 | - | - | 11.6 | 30.2 |
| [YOLOv8m-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-pose.pt) | 640 | - | 63.6 | - | - | 26.4 | 81.0 |
| [YOLOv8l-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-pose.pt) | 640 | - | 67.0 | - | - | 44.4 | 168.6 |
| [YOLOv8x-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose.pt) | 640 | - | 68.9 | - | - | 69.4 | 263.2 |
| [YOLOv8x-pose-p6](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose-p6.pt) | 1280 | - | 71.5 | - | - | 99.1 | 1066.4 |
| [YOLOv8n-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-pose.pt) | 640 | - | 49.7 | 131.8 | 1.18 | 3.3 | 9.2 |
| [YOLOv8s-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-pose.pt) | 640 | - | 59.2 | 233.2 | 1.42 | 11.6 | 30.2 |
| [YOLOv8m-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-pose.pt) | 640 | - | 63.6 | 456.3 | 2.00 | 26.4 | 81.0 |
| [YOLOv8l-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-pose.pt) | 640 | - | 67.0 | 784.5 | 2.59 | 44.4 | 168.6 |
| [YOLOv8x-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose.pt) | 640 | - | 68.9 | 1607.1 | 3.73 | 69.4 | 263.2 |
| [YOLOv8x-pose-p6](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose-p6.pt) | 1280 | - | 71.5 | 4088.7 | 10.04 | 99.1 | 1066.4 |
- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO Keypoints val2017](http://cocodataset.org)
dataset.
@ -123,7 +123,7 @@ Use a trained YOLOv8n-pose model to run predictions on images.
=== "CLI"
```bash
yolo pose predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg' # predict with official model
yolo pose predict model=yolov8n-pose.pt source='https://ultralytics.com/images/bus.jpg' # predict with official model
yolo pose predict model=path/to/best.pt source='https://ultralytics.com/images/bus.jpg' # predict with custom model
```
@ -131,7 +131,7 @@ See full `predict` mode details in the [Predict](https://docs.ultralytics.com/mo
## Export
Export a YOLOv8n model to a different format like ONNX, CoreML, etc.
Export a YOLOv8n Pose model to a different format like ONNX, CoreML, etc.
!!! example ""
@ -141,7 +141,7 @@ Export a YOLOv8n model to a different format like ONNX, CoreML, etc.
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n.pt') # load an official model
model = YOLO('yolov8n-pose.pt') # load an official model
model = YOLO('path/to/best.pt') # load a custom trained
# Export the model
@ -150,7 +150,7 @@ Export a YOLOv8n model to a different format like ONNX, CoreML, etc.
=== "CLI"
```bash
yolo export model=yolov8n.pt format=onnx # export official model
yolo export model=yolov8n-pose.pt format=onnx # export official model
yolo export model=path/to/best.pt format=onnx # export custom trained model
```