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ultralytics 8.0.224 Counting and Heatmaps updates (#6855)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Muhammad Rizwan Munawar <chr043416@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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Glenn Jocher 2023-12-08 02:34:32 +01:00 committed by GitHub
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5
.pre-commit-config.yaml
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@ -50,7 +50,10 @@ repos:
name: MD formatting
additional_dependencies:
- mdformat-gfm
- mdformat-black
# - mdformat-black
# - mdformat-frontmatter
args:
- --wrap=no
exclude: 'docs/.*\.md'
# exclude: "README.md|README.zh-CN.md|CONTRIBUTING.md"

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CONTRIBUTING.md
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@ -1,4 +1,4 @@
## Contributing to YOLOv8 🚀
# Contributing to YOLOv8 🚀
We love your input! We want to make contributing to YOLOv8 as easy and transparent as possible, whether it's:
@ -8,8 +8,7 @@ We love your input! We want to make contributing to YOLOv8 as easy and transpare
- Proposing a new feature
- Becoming a maintainer
YOLOv8 works so well due to our combined community effort, and for every small improvement you contribute you will be
helping push the frontiers of what's possible in AI 😃!
YOLOv8 works so well due to our combined community effort, and for every small improvement you contribute you will be helping push the frontiers of what's possible in AI 😃!
## Submitting a Pull Request (PR) 🛠️
@ -35,9 +34,7 @@ Change `matplotlib` version from `3.2.2` to `3.3`.
### 4. Preview Changes and Submit PR
Click on the **Preview changes** tab to verify your updates. At the bottom of the screen select 'Create a **new branch**
for this commit', assign your branch a descriptive name such as `fix/matplotlib_version` and click the green **Propose
changes** button. All done, your PR is now submitted to YOLOv8 for review and approval 😃!
Click on the **Preview changes** tab to verify your updates. At the bottom of the screen select 'Create a **new branch** for this commit', assign your branch a descriptive name such as `fix/matplotlib_version` and click the green **Propose changes** button. All done, your PR is now submitted to YOLOv8 for review and approval 😃!
<p align="center"><img width="800" alt="PR_step4" src="https://user-images.githubusercontent.com/26833433/122260856-0b208000-ced4-11eb-8e8e-77b6151cbcc3.png"></p>
@ -45,8 +42,7 @@ changes** button. All done, your PR is now submitted to YOLOv8 for review and ap
To allow your work to be integrated as seamlessly as possible, we advise you to:
- ✅ Verify your PR is **up-to-date** with `ultralytics/ultralytics` `main` branch. If your PR is behind you can update
your code by clicking the 'Update branch' button or by running `git pull` and `git merge main` locally.
- ✅ Verify your PR is **up-to-date** with `ultralytics/ultralytics` `main` branch. If your PR is behind you can update your code by clicking the 'Update branch' button or by running `git pull` and `git merge main` locally.
<p align="center"><img width="751" alt="PR recommendation 1" src="https://user-images.githubusercontent.com/26833433/187295893-50ed9f44-b2c9-4138-a614-de69bd1753d7.png"></p>
@ -54,14 +50,11 @@ To allow your work to be integrated as seamlessly as possible, we advise you to:
<p align="center"><img width="751" alt="PR recommendation 2" src="https://user-images.githubusercontent.com/26833433/187296922-545c5498-f64a-4d8c-8300-5fa764360da6.png"></p>
- ✅ Reduce changes to the absolute **minimum** required for your bug fix or feature addition. _"It is not daily increase
but daily decrease, hack away the unessential. The closer to the source, the less wastage there is."_ — Bruce Lee
- ✅ Reduce changes to the absolute **minimum** required for your bug fix or feature addition. _"It is not daily increase but daily decrease, hack away the unessential. The closer to the source, the less wastage there is."_ — Bruce Lee
### Docstrings
Not all functions or classes require docstrings but when they do, we
follow [google-style docstrings format](https://google.github.io/styleguide/pyguide.html#38-comments-and-docstrings).
Here is an example:
Not all functions or classes require docstrings but when they do, we follow [google-style docstrings format](https://google.github.io/styleguide/pyguide.html#38-comments-and-docstrings). Here is an example:
```python
"""
@ -83,33 +76,21 @@ Here is an example:
If you spot a problem with YOLOv8 please submit a Bug Report!
For us to start investigating a possible problem we need to be able to reproduce it ourselves first. We've created a few
short guidelines below to help users provide what we need in order to get started.
For us to start investigating a possible problem we need to be able to reproduce it ourselves first. We've created a few short guidelines below to help users provide what we need in order to get started.
When asking a question, people will be better able to provide help if you provide **code** that they can easily
understand and use to **reproduce** the problem. This is referred to by community members as creating
a [minimum reproducible example](https://docs.ultralytics.com/help/minimum_reproducible_example/). Your code that reproduces
the problem should be:
When asking a question, people will be better able to provide help if you provide **code** that they can easily understand and use to **reproduce** the problem. This is referred to by community members as creating a [minimum reproducible example](https://docs.ultralytics.com/help/minimum_reproducible_example/). Your code that reproduces the problem should be:
- ✅ **Minimal** – Use as little code as possible that still produces the same problem
- ✅ **Complete** – Provide **all** parts someone else needs to reproduce your problem in the question itself
- ✅ **Reproducible** – Test the code you're about to provide to make sure it reproduces the problem
In addition to the above requirements, for [Ultralytics](https://ultralytics.com/) to provide assistance your code
should be:
In addition to the above requirements, for [Ultralytics](https://ultralytics.com/) to provide assistance your code should be:
- ✅ **Current** – Verify that your code is up-to-date with current
GitHub [main](https://github.com/ultralytics/ultralytics/tree/main) branch, and if necessary `git pull` or `git clone`
a new copy to ensure your problem has not already been resolved by previous commits.
- ✅ **Unmodified** – Your problem must be reproducible without any modifications to the codebase in this
repository. [Ultralytics](https://ultralytics.com/) does not provide support for custom code ⚠️.
- ✅ **Current** – Verify that your code is up-to-date with current GitHub [main](https://github.com/ultralytics/ultralytics/tree/main) branch, and if necessary `git pull` or `git clone` a new copy to ensure your problem has not already been resolved by previous commits.
- ✅ **Unmodified** – Your problem must be reproducible without any modifications to the codebase in this repository. [Ultralytics](https://ultralytics.com/) does not provide support for custom code ⚠️.
If you believe your problem meets all of the above criteria, please close this issue and raise a new one using the 🐛
**Bug Report** [template](https://github.com/ultralytics/ultralytics/issues/new/choose) and providing
a [minimum reproducible example](https://docs.ultralytics.com/help/minimum_reproducible_example/) to help us better
understand and diagnose your problem.
If you believe your problem meets all of the above criteria, please close this issue and raise a new one using the 🐛 **Bug Report** [template](https://github.com/ultralytics/ultralytics/issues/new/choose) and providing a [minimum reproducible example](https://docs.ultralytics.com/help/minimum_reproducible_example/) to help us better understand and diagnose your problem.
## License
By contributing, you agree that your contributions will be licensed under
the [AGPL-3.0 license](https://choosealicense.com/licenses/agpl-3.0/)
By contributing, you agree that your contributions will be licensed under the [AGPL-3.0 license](https://choosealicense.com/licenses/agpl-3.0/)

34
README.md
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@ -4,8 +4,7 @@
<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/im/banner-yolo-vision-2023.png" alt="YOLO Vision banner"></a>
</p>
[中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [हिन्दी](https://docs.ultralytics.com/hi/) | [العربية](https://docs.ultralytics.com/ar/)
<br>
[中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [हिन्दी](https://docs.ultralytics.com/hi/) | [العربية](https://docs.ultralytics.com/ar/) <br>
<div>
<a href="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml"><img src="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml/badge.svg" alt="Ultralytics CI"></a>
@ -119,10 +118,8 @@ See [Detection Docs](https://docs.ultralytics.com/tasks/detect/) for usage examp
| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l.pt) | 640 | 52.9 | 375.2 | 2.39 | 43.7 | 165.2 |
| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x.pt) | 640 | 53.9 | 479.1 | 3.53 | 68.2 | 257.8 |
- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.
<br>Reproduce by `yolo val detect data=coco.yaml device=0`
- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance.
<br>Reproduce by `yolo val detect data=coco.yaml batch=1 device=0|cpu`
- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset. <br>Reproduce by `yolo val detect data=coco.yaml device=0`
- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val detect data=coco.yaml batch=1 device=0|cpu`
</details>
@ -138,10 +135,8 @@ See [Detection Docs](https://docs.ultralytics.com/tasks/detect/) for usage examp
| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-oiv7.pt) | 640 | 34.9 | 596.9 | 2.43 | 44.1 | 167.4 |
| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-oiv7.pt) | 640 | 36.3 | 860.6 | 3.56 | 68.7 | 260.6 |
- **mAP<sup>val</sup>** values are for single-model single-scale on [Open Image V7](https://docs.ultralytics.com/datasets/detect/open-images-v7/) dataset.
<br>Reproduce by `yolo val detect data=open-images-v7.yaml device=0`
- **Speed** averaged over Open Image V7 val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance.
<br>Reproduce by `yolo val detect data=open-images-v7.yaml batch=1 device=0|cpu`
- **mAP<sup>val</sup>** values are for single-model single-scale on [Open Image V7](https://docs.ultralytics.com/datasets/detect/open-images-v7/) dataset. <br>Reproduce by `yolo val detect data=open-images-v7.yaml device=0`
- **Speed** averaged over Open Image V7 val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val detect data=open-images-v7.yaml batch=1 device=0|cpu`
</details>
@ -157,10 +152,8 @@ See [Segmentation Docs](https://docs.ultralytics.com/tasks/segment/) for usage e
| [YOLOv8l-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-seg.pt) | 640 | 52.3 | 42.6 | 572.4 | 2.79 | 46.0 | 220.5 |
| [YOLOv8x-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-seg.pt) | 640 | 53.4 | 43.4 | 712.1 | 4.02 | 71.8 | 344.1 |
- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.
<br>Reproduce by `yolo val segment data=coco-seg.yaml device=0`
- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance.
<br>Reproduce by `yolo val segment data=coco-seg.yaml batch=1 device=0|cpu`
- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset. <br>Reproduce by `yolo val segment data=coco-seg.yaml device=0`
- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val segment data=coco-seg.yaml batch=1 device=0|cpu`
</details>
@ -177,11 +170,8 @@ See [Pose Docs](https://docs.ultralytics.com/tasks/pose/) for usage examples wit
| [YOLOv8x-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose.pt) | 640 | 69.2 | 90.2 | 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 | 71.6 | 91.2 | 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.
<br>Reproduce by `yolo val pose data=coco-pose.yaml device=0`
- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance.
<br>Reproduce by `yolo val pose data=coco-pose.yaml batch=1 device=0|cpu`
- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO Keypoints val2017](http://cocodataset.org) dataset. <br>Reproduce by `yolo val pose data=coco-pose.yaml device=0`
- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val pose data=coco-pose.yaml batch=1 device=0|cpu`
</details>
@ -197,10 +187,8 @@ See [Classification Docs](https://docs.ultralytics.com/tasks/classify/) for usag
| [YOLOv8l-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-cls.pt) | 224 | 78.0 | 94.1 | 163.0 | 0.87 | 37.5 | 99.7 |
| [YOLOv8x-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-cls.pt) | 224 | 78.4 | 94.3 | 232.0 | 1.01 | 57.4 | 154.8 |
- **acc** values are model accuracies on the [ImageNet](https://www.image-net.org/) dataset validation set.
<br>Reproduce by `yolo val classify data=path/to/ImageNet device=0`
- **Speed** averaged over ImageNet val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance.
<br>Reproduce by `yolo val classify data=path/to/ImageNet batch=1 device=0|cpu`
- **acc** values are model accuracies on the [ImageNet](https://www.image-net.org/) dataset validation set. <br>Reproduce by `yolo val classify data=path/to/ImageNet device=0`
- **Speed** averaged over ImageNet val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val classify data=path/to/ImageNet batch=1 device=0|cpu`
</details>

33
README.zh-CN.md
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@ -4,8 +4,7 @@
<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/im/banner-yolo-vision-2023.png" alt="YOLO Vision banner"></a>
</p>
[中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [हिन्दी](https://docs.ultralytics.com/hi/) | [العربية](https://docs.ultralytics.com/ar/)
<br>
[中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [हिन्दी](https://docs.ultralytics.com/hi/) | [العربية](https://docs.ultralytics.com/ar/) <br>
<div>
<a href="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml"><img src="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml/badge.svg" alt="Ultralytics CI"></a>
@ -119,10 +118,8 @@ success = model.export(format="onnx") # 将模型导出为 ONNX 格式
| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l.pt) | 640 | 52.9 | 375.2 | 2.39 | 43.7 | 165.2 |
| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x.pt) | 640 | 53.9 | 479.1 | 3.53 | 68.2 | 257.8 |
- **mAP<sup>val</sup>** 值是基于单模型单尺度在 [COCO val2017](http://cocodataset.org) 数据集上的结果。
<br>通过 `yolo val detect data=coco.yaml device=0` 复现
- **速度** 是使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例对 COCO val 图像进行平均计算的。
<br>通过 `yolo val detect data=coco.yaml batch=1 device=0|cpu` 复现
- **mAP<sup>val</sup>** 值是基于单模型单尺度在 [COCO val2017](http://cocodataset.org) 数据集上的结果。 <br>通过 `yolo val detect data=coco.yaml device=0` 复现
- **速度** 是使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例对 COCO val 图像进行平均计算的。 <br>通过 `yolo val detect data=coco.yaml batch=1 device=0|cpu` 复现
</details>
@ -138,10 +135,8 @@ success = model.export(format="onnx") # 将模型导出为 ONNX 格式
| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-oiv7.pt) | 640 | 34.9 | 596.9 | 2.43 | 44.1 | 167.4 |
| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-oiv7.pt) | 640 | 36.3 | 860.6 | 3.56 | 68.7 | 260.6 |
- **mAP<sup>验证</sup>** 值适用于在[Open Image V7](https://docs.ultralytics.com/datasets/detect/open-images-v7/)数据集上的单模型单尺度。
<br>通过 `yolo val detect data=open-images-v7.yaml device=0` 以复现。
- **速度** 在使用[Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/)实例对Open Image V7验证图像进行平均测算。
<br>通过 `yolo val detect data=open-images-v7.yaml batch=1 device=0|cpu` 以复现。
- **mAP<sup>验证</sup>** 值适用于在[Open Image V7](https://docs.ultralytics.com/datasets/detect/open-images-v7/)数据集上的单模型单尺度。 <br>通过 `yolo val detect data=open-images-v7.yaml device=0` 以复现。
- **速度** 在使用[Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/)实例对Open Image V7验证图像进行平均测算。 <br>通过 `yolo val detect data=open-images-v7.yaml batch=1 device=0|cpu` 以复现。
</details>
@ -157,10 +152,8 @@ success = model.export(format="onnx") # 将模型导出为 ONNX 格式
| [YOLOv8l-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-seg.pt) | 640 | 52.3 | 42.6 | 572.4 | 2.79 | 46.0 | 220.5 |
| [YOLOv8x-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-seg.pt) | 640 | 53.4 | 43.4 | 712.1 | 4.02 | 71.8 | 344.1 |
- **mAP<sup>val</sup>** 值是基于单模型单尺度在 [COCO val2017](http://cocodataset.org) 数据集上的结果。
<br>通过 `yolo val segment data=coco-seg.yaml device=0` 复现
- **速度** 是使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例对 COCO val 图像进行平均计算的。
<br>通过 `yolo val segment data=coco-seg.yaml batch=1 device=0|cpu` 复现
- **mAP<sup>val</sup>** 值是基于单模型单尺度在 [COCO val2017](http://cocodataset.org) 数据集上的结果。 <br>通过 `yolo val segment data=coco-seg.yaml device=0` 复现
- **速度** 是使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例对 COCO val 图像进行平均计算的。 <br>通过 `yolo val segment data=coco-seg.yaml batch=1 device=0|cpu` 复现
</details>
@ -177,10 +170,8 @@ success = model.export(format="onnx") # 将模型导出为 ONNX 格式
| [YOLOv8x-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose.pt) | 640 | 69.2 | 90.2 | 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 | 71.6 | 91.2 | 4088.7 | 10.04 | 99.1 | 1066.4 |
- **mAP<sup>val</sup>** 值是基于单模型单尺度在 [COCO Keypoints val2017](http://cocodataset.org) 数据集上的结果。
<br>通过 `yolo val pose data=coco-pose.yaml device=0` 复现
- **速度** 是使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例对 COCO val 图像进行平均计算的。
<br>通过 `yolo val pose data=coco-pose.yaml batch=1 device=0|cpu` 复现
- **mAP<sup>val</sup>** 值是基于单模型单尺度在 [COCO Keypoints val2017](http://cocodataset.org) 数据集上的结果。 <br>通过 `yolo val pose data=coco-pose.yaml device=0` 复现
- **速度** 是使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例对 COCO val 图像进行平均计算的。 <br>通过 `yolo val pose data=coco-pose.yaml batch=1 device=0|cpu` 复现
</details>
@ -196,10 +187,8 @@ success = model.export(format="onnx") # 将模型导出为 ONNX 格式
| [YOLOv8l-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-cls.pt) | 224 | 78.0 | 94.1 | 163.0 | 0.87 | 37.5 | 99.7 |
| [YOLOv8x-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-cls.pt) | 224 | 78.4 | 94.3 | 232.0 | 1.01 | 57.4 | 154.8 |
- **acc** 值是模型在 [ImageNet](https://www.image-net.org/) 数据集验证集上的准确率。
<br>通过 `yolo val classify data=path/to/ImageNet device=0` 复现
- **速度** 是使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例对 ImageNet val 图像进行平均计算的。
<br>通过 `yolo val classify data=path/to/ImageNet batch=1 device=0|cpu` 复现
- **acc** 值是模型在 [ImageNet](https://www.image-net.org/) 数据集验证集上的准确率。 <br>通过 `yolo val classify data=path/to/ImageNet device=0` 复现
- **速度** 是使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例对 ImageNet val 图像进行平均计算的。 <br>通过 `yolo val classify data=path/to/ImageNet batch=1 device=0|cpu` 复现
</details>

8
docs/README.md
View File

@ -4,7 +4,7 @@ Ultralytics Docs are deployed to [https://docs.ultralytics.com](https://docs.ult
[![pages-build-deployment](https://github.com/ultralytics/docs/actions/workflows/pages/pages-build-deployment/badge.svg)](https://github.com/ultralytics/docs/actions/workflows/pages/pages-build-deployment) [![Check Broken links](https://github.com/ultralytics/docs/actions/workflows/links.yml/badge.svg)](https://github.com/ultralytics/docs/actions/workflows/links.yml)
### Install Ultralytics package
## Install Ultralytics package
[![PyPI version](https://badge.fury.io/py/ultralytics.svg)](https://badge.fury.io/py/ultralytics) [![Downloads](https://static.pepy.tech/badge/ultralytics)](https://pepy.tech/project/ultralytics)
@ -32,7 +32,7 @@ This will install the ultralytics package and its dependencies in developer mode
Note that you may need to use the pip3 command instead of pip if you have multiple versions of Python installed on your system.
### Building and Serving Locally
## Building and Serving Locally
The `mkdocs serve` command is used to build and serve a local version of the MkDocs documentation site. It is typically used during the development and testing phase of a documentation project.
@ -52,7 +52,7 @@ While the site is being served, you can make changes to the documentation files
To stop the serve command and terminate the local server, you can use the `CTRL+C` keyboard shortcut.
### Building and Serving Multi-Language
## Building and Serving Multi-Language
For multi-language MkDocs sites use the following additional steps:
@ -81,7 +81,7 @@ For multi-language MkDocs sites use the following additional steps:
Note the above steps are combined into the Ultralytics [build_docs.py](https://github.com/ultralytics/ultralytics/blob/main/docs/build_docs.py) script.
### Deploying Your Documentation Site
## Deploying Your Documentation Site
To deploy your MkDocs documentation site, you will need to choose a hosting provider and a deployment method. Some popular options include GitHub Pages, GitLab Pages, and Amazon S3.

113
docs/en/guides/heatmaps.md
View File

@ -35,12 +35,11 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
from ultralytics.solutions import heatmap
import cv2
model = YOLO("yolov8s.pt")
model = YOLO("yolov8s.pt") # YOLOv8 custom/pretrained model
cap = cv2.VideoCapture("path/to/video/file.mp4")
if not cap.isOpened():
print("Error reading video file")
exit(0)
assert cap.isOpened(), "Error reading video file"
# Heatmap Init
heatmap_obj = heatmap.Heatmap()
heatmap_obj.set_args(colormap=cv2.COLORMAP_CIVIDIS,
imw=cap.get(4), # should same as im0 width
@ -52,7 +51,7 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
if not success:
exit(0)
results = model.track(im0, persist=True)
frame = heatmap_obj.generate_heatmap(im0, tracks=results)
im0 = heatmap_obj.generate_heatmap(im0, tracks=results)
```
@ -62,14 +61,13 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
from ultralytics.solutions import heatmap
import cv2
model = YOLO("yolov8s.pt")
model = YOLO("yolov8s.pt") # YOLOv8 custom/pretrained model
cap = cv2.VideoCapture("path/to/video/file.mp4")
if not cap.isOpened():
print("Error reading video file")
exit(0)
assert cap.isOpened(), "Error reading video file"
classes_for_heatmap = [0, 2]
# Heatmap init
heatmap_obj = heatmap.Heatmap()
heatmap_obj.set_args(colormap=cv2.COLORMAP_CIVIDIS,
imw=cap.get(4), # should same as im0 width
@ -80,29 +78,28 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
success, im0 = cap.read()
if not success:
exit(0)
results = model.track(im0, persist=True,
classes=classes_for_heatmap)
frame = heatmap_obj.generate_heatmap(im0, tracks=results)
results = model.track(im0, persist=True, classes=classes_for_heatmap)
im0 = heatmap_obj.generate_heatmap(im0, tracks=results)
```
=== "Heatmap with Save Output"
```python
from ultralytics import YOLO
import heatmap
from ultralytics.solutions import heatmap
import cv2
model = YOLO("yolov8n.pt")
model = YOLO("yolov8s.pt") # YOLOv8 custom/pretrained model
cap = cv2.VideoCapture("path/to/video/file.mp4")
if not cap.isOpened():
print("Error reading video file")
exit(0)
assert cap.isOpened(), "Error reading video file"
# Video writer
video_writer = cv2.VideoWriter("heatmap_output.avi",
cv2.VideoWriter_fourcc(*'mp4v'),
int(cap.get(5)),
(int(cap.get(3)), int(cap.get(4))))
# Heatmap init
heatmap_obj = heatmap.Heatmap()
heatmap_obj.set_args(colormap=cv2.COLORMAP_CIVIDIS,
imw=cap.get(4), # should same as im0 width
@ -113,22 +110,55 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
success, im0 = cap.read()
if not success:
exit(0)
results = model.track(im0, persist=True)
frame = heatmap_obj.generate_heatmap(im0, tracks=results)
results = model.track(im0, persist=True, classes=classes_for_heatmap)
im0 = heatmap_obj.generate_heatmap(im0, tracks=results)
video_writer.write(im0)
video_writer.release()
```
=== "Heatmap with Object Counting"
```python
from ultralytics import YOLO
from ultralytics.solutions import heatmap
import cv2
model = YOLO("yolov8s.pt") # YOLOv8 custom/pretrained model
cap = cv2.VideoCapture("path/to/video/file.mp4") # Video file Path, webcam 0
assert cap.isOpened(), "Error reading video file"
# Region for object counting
count_reg_pts = [(20, 400), (1080, 404), (1080, 360), (20, 360)]
# Heatmap Init
heatmap_obj = heatmap.Heatmap()
heatmap_obj.set_args(colormap=cv2.COLORMAP_JET,
imw=cap.get(4), # should same as im0 width
imh=cap.get(3), # should same as im0 height
view_img=True,
count_reg_pts=count_reg_pts)
while cap.isOpened():
success, im0 = cap.read()
if not success:
exit(0)
results = model.track(im0, persist=True)
im0 = heatmap_obj.generate_heatmap(im0, tracks=results)
```
### Arguments `set_args`
| Name | Type | Default | Description |
|---------------|----------------|---------|--------------------------------|
| view_img | `bool` | `False` | Display the frame with heatmap |
| colormap | `cv2.COLORMAP` | `None` | cv2.COLORMAP for heatmap |
| imw | `int` | `None` | Width of Heatmap |
| imh | `int` | `None` | Height of Heatmap |
| heatmap_alpha | `float` | `0.5` | Heatmap alpha value |
| Name | Type | Default | Description |
|---------------------|----------------|-----------------|---------------------------------|
| view_img | `bool` | `False` | Display the frame with heatmap |
| colormap | `cv2.COLORMAP` | `None` | cv2.COLORMAP for heatmap |
| imw | `int` | `None` | Width of Heatmap |
| imh | `int` | `None` | Height of Heatmap |
| heatmap_alpha | `float` | `0.5` | Heatmap alpha value |
| count_reg_pts | `list` | `None` | Object counting region points |
| count_txt_thickness | `int` | `2` | Count values text size |
| count_reg_color | `tuple` | `(255, 0, 255)` | Counting region color |
| region_thickness | `int` | `5` | Counting region thickness value |
### Arguments `model.track`
@ -140,3 +170,32 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
| `conf` | `float` | `0.3` | Confidence Threshold |
| `iou` | `float` | `0.5` | IOU Threshold |
| `classes` | `list` | `None` | filter results by class, i.e. classes=0, or classes=[0,2,3] |
### Heatmap COLORMAPs
| Colormap Name | Description |
|---------------------------------|----------------------------------------|
| `cv::COLORMAP_AUTUMN` | Autumn color map |
| `cv::COLORMAP_BONE` | Bone color map |
| `cv::COLORMAP_JET` | Jet color map |
| `cv::COLORMAP_WINTER` | Winter color map |
| `cv::COLORMAP_RAINBOW` | Rainbow color map |
| `cv::COLORMAP_OCEAN` | Ocean color map |
| `cv::COLORMAP_SUMMER` | Summer color map |
| `cv::COLORMAP_SPRING` | Spring color map |
| `cv::COLORMAP_COOL` | Cool color map |
| `cv::COLORMAP_HSV` | HSV (Hue, Saturation, Value) color map |
| `cv::COLORMAP_PINK` | Pink color map |
| `cv::COLORMAP_HOT` | Hot color map |
| `cv::COLORMAP_PARULA` | Parula color map |
| `cv::COLORMAP_MAGMA` | Magma color map |
| `cv::COLORMAP_INFERNO` | Inferno color map |
| `cv::COLORMAP_PLASMA` | Plasma color map |
| `cv::COLORMAP_VIRIDIS` | Viridis color map |
| `cv::COLORMAP_CIVIDIS` | Cividis color map |
| `cv::COLORMAP_TWILIGHT` | Twilight color map |
| `cv::COLORMAP_TWILIGHT_SHIFTED` | Shifted Twilight color map |
| `cv::COLORMAP_TURBO` | Turbo color map |
| `cv::COLORMAP_DEEPGREEN` | Deep Green color map |
These colormaps are commonly used for visualizing data with different color representations.

14
docs/en/guides/object-counting.md
View File

@ -23,7 +23,6 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
| ![Conveyor Belt Packets Counting Using Ultralytics YOLOv8](https://github.com/RizwanMunawar/ultralytics/assets/62513924/70e2d106-510c-4c6c-a57a-d34a765aa757) | ![Fish Counting in Sea using Ultralytics YOLOv8](https://github.com/RizwanMunawar/ultralytics/assets/62513924/c60d047b-3837-435f-8d29-bb9fc95d2191) |
| Conveyor Belt Packets Counting Using Ultralytics YOLOv8 | Fish Counting in Sea using Ultralytics YOLOv8 |
!!! Example "Object Counting Example"
=== "Object Counting"
@ -34,9 +33,7 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
model = YOLO("yolov8n.pt")
cap = cv2.VideoCapture("path/to/video/file.mp4")
if not cap.isOpened():
print("Error reading video file")
exit(0)
assert cap.isOpened(), "Error reading video file"
counter = object_counter.ObjectCounter() # Init Object Counter
region_points = [(20, 400), (1080, 404), (1080, 360), (20, 360)]
@ -61,9 +58,7 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
model = YOLO("yolov8n.pt")
cap = cv2.VideoCapture("path/to/video/file.mp4")
if not cap.isOpened():
print("Error reading video file")
exit(0)
assert cap.isOpened(), "Error reading video file"
classes_to_count = [0, 2]
counter = object_counter.ObjectCounter() # Init Object Counter
@ -91,9 +86,7 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
model = YOLO("yolov8n.pt")
cap = cv2.VideoCapture("path/to/video/file.mp4")
if not cap.isOpened():
print("Error reading video file")
exit(0)
assert cap.isOpened(), "Error reading video file"
video_writer = cv2.VideoWriter("object_counting.avi",
cv2.VideoWriter_fourcc(*'mp4v'),
@ -134,7 +127,6 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
| track_thickness | `int` | `2` | Tracking line thickness |
| draw_tracks | `bool` | `False` | Draw Tracks lines |
### Arguments `model.track`
| Name | Type | Default | Description |

9
docs/en/guides/workouts-monitoring.md
View File

@ -23,7 +23,6 @@ Monitoring workouts through pose estimation with [Ultralytics YOLOv8](https://gi
| ![PushUps Counting](https://github.com/RizwanMunawar/ultralytics/assets/62513924/cf016a41-589f-420f-8a8c-2cc8174a16de) | ![PullUps Counting](https://github.com/RizwanMunawar/ultralytics/assets/62513924/cb20f316-fac2-4330-8445-dcf5ffebe329) |
| PushUps Counting | PullUps Counting |
!!! Example "Workouts Monitoring Example"
=== "Workouts Monitoring"
@ -34,9 +33,7 @@ Monitoring workouts through pose estimation with [Ultralytics YOLOv8](https://gi
model = YOLO("yolov8n-pose.pt")
cap = cv2.VideoCapture("path/to/video/file.mp4")
if not cap.isOpened():
print("Error reading video file")
exit(0)
assert cap.isOpened(), "Error reading video file"
gym_object = ai_gym.AIGym() # init AI GYM module
gym_object.set_args(line_thickness=2,
@ -62,9 +59,7 @@ Monitoring workouts through pose estimation with [Ultralytics YOLOv8](https://gi
model = YOLO("yolov8n-pose.pt")
cap = cv2.VideoCapture("path/to/video/file.mp4")
if not cap.isOpened():
print("Error reading video file")
exit(0)
assert cap.isOpened(), "Error reading video file"
video_writer = cv2.VideoWriter("workouts.avi",
cv2.VideoWriter_fourcc(*'mp4v'),

1
docs/en/yolov5/tutorials/clearml_logging_integration.md
View File

@ -90,7 +90,6 @@ This will capture:
- Mosaic per epoch
- Validation images per epoch
That's a lot right? 🤯 Now, we can visualize all of this information in the ClearML UI to get an overview of our training progress. Add custom columns to the table view (such as e.g. mAP_0.5) so you can easily sort on the best performing model. Or select multiple experiments and directly compare them!
There even more we can do with all of this information, like hyperparameter optimization and remote execution, so keep reading if you want to see how that works!

3
examples/YOLOv8-ONNXRuntime-Rust/README.md
View File

@ -155,8 +155,7 @@ cargo run --release -- --help
### Classification
Running dynamic shape ONNX model on `CPU` with image size `--height 224 --width 224`.
Saving plotted image in `runs` directory.
Running dynamic shape ONNX model on `CPU` with image size `--height 224 --width 224`. Saving plotted image in `runs` directory.
```
cargo run --release -- --model ../assets/weights/yolov8m-cls-dyn.onnx --source ../assets/images/dog.jpg --height 224 --width 224 --plot --profile

2
ultralytics/__init__.py
View File

@ -1,6 +1,6 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
__version__ = '8.0.223'
__version__ = '8.0.224'
from ultralytics.models import RTDETR, SAM, YOLO
from ultralytics.models.fastsam import FastSAM

3
ultralytics/cfg/models/README.md
View File

@ -14,8 +14,7 @@ Model `*.yaml` files may be used directly in the Command Line Interface (CLI) wi
yolo task=detect mode=train model=yolov8n.yaml data=coco128.yaml epochs=100
```
They may also be used directly in a Python environment, and accepts the same
[arguments](https://docs.ultralytics.com/usage/cfg/) as in the CLI example above:
They may also be used directly in a Python environment, and accepts the same [arguments](https://docs.ultralytics.com/usage/cfg/) as in the CLI example above:
```python
from ultralytics import YOLO

93
ultralytics/solutions/heatmap.py
View File

@ -1,14 +1,24 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
from collections import defaultdict
import cv2
import numpy as np
from ultralytics.utils.checks import check_requirements
from ultralytics.utils.plotting import Annotator
check_requirements('shapely>=2.0.0')
from shapely.geometry import Polygon
from shapely.geometry.point import Point
class Heatmap:
"""A class to draw heatmaps in real-time video stream based on their tracks."""
def __init__(self):
"""Initializes the heatmap class with default values for Visual, Image, track and heatmap parameters."""
"""Initializes the heatmap class with default values for Visual, Image, track, count and heatmap parameters."""
# Visual Information
self.annotator = None
@ -28,8 +38,28 @@ class Heatmap:
self.boxes = None
self.track_ids = None
self.clss = None
self.track_history = None
def set_args(self, imw, imh, colormap=cv2.COLORMAP_JET, heatmap_alpha=0.5, view_img=False):
# Counting Info
self.count_reg_pts = None
self.count_region = None
self.in_counts = 0
self.out_counts = 0
self.count_list = []
self.count_txt_thickness = 0
self.count_reg_color = (0, 255, 0)
self.region_thickness = 5
def set_args(self,
imw,
imh,
colormap=cv2.COLORMAP_JET,
heatmap_alpha=0.5,
view_img=False,
count_reg_pts=None,
count_txt_thickness=2,
count_reg_color=(255, 0, 255),
region_thickness=5):
"""
Configures the heatmap colormap, width, height and display parameters.
@ -39,6 +69,10 @@ class Heatmap:
imh (int): The height of the frame.
heatmap_alpha (float): alpha value for heatmap display
view_img (bool): Flag indicating frame display
count_reg_pts (list): Object counting region points
count_txt_thickness (int): Text thickness for object counting display
count_reg_color (RGB color): Color of object counting region
region_thickness (int): Object counting Region thickness
"""
self.imw = imw
self.imh = imh
@ -46,8 +80,16 @@ class Heatmap:
self.heatmap_alpha = heatmap_alpha
self.view_img = view_img
# Heatmap new frame
self.heatmap = np.zeros((int(self.imw), int(self.imh)), dtype=np.float32)
self.heatmap = np.zeros((int(self.imw), int(self.imh)), dtype=np.float32) # Heatmap new frame
if count_reg_pts is not None:
self.track_history = defaultdict(list)
self.count_reg_pts = count_reg_pts
self.count_region = Polygon(self.count_reg_pts)
self.count_txt_thickness = count_txt_thickness # Counting text thickness
self.count_reg_color = count_reg_color
self.region_thickness = region_thickness
def extract_results(self, tracks):
"""
@ -56,8 +98,6 @@ class Heatmap:
Args:
tracks (list): List of tracks obtained from the object tracking process.
"""
if tracks[0].boxes.id is None:
return
self.boxes = tracks[0].boxes.xyxy.cpu()
self.clss = tracks[0].boxes.cls.cpu().tolist()
self.track_ids = tracks[0].boxes.id.int().cpu().tolist()
@ -70,15 +110,49 @@ class Heatmap:
im0 (nd array): Image
tracks (list): List of tracks obtained from the object tracking process.
"""
self.extract_results(tracks)
self.im0 = im0
if tracks[0].boxes.id is None:
return self.im0
for box, cls in zip(self.boxes, self.clss):
self.heatmap[int(box[1]):int(box[3]), int(box[0]):int(box[2])] += 1
self.extract_results(tracks)
self.annotator = Annotator(self.im0, self.count_txt_thickness, None)
if self.count_reg_pts is not None:
# Draw counting region
self.annotator.draw_region(reg_pts=self.count_reg_pts,
color=self.count_reg_color,
thickness=self.region_thickness)
for box, cls, track_id in zip(self.boxes, self.clss, self.track_ids):
self.heatmap[int(box[1]):int(box[3]), int(box[0]):int(box[2])] += 1
# Store tracking hist
track_line = self.track_history[track_id]
track_line.append((float((box[0] + box[2]) / 2), float((box[1] + box[3]) / 2)))
if len(track_line) > 30:
track_line.pop(0)
# Count objects
if self.count_region.contains(Point(track_line[-1])):
if track_id not in self.count_list:
self.count_list.append(track_id)
if box[0] < self.count_region.centroid.x:
self.out_counts += 1
else:
self.in_counts += 1
else:
for box, cls in zip(self.boxes, self.clss):
self.heatmap[int(box[1]):int(box[3]), int(box[0]):int(box[2])] += 1
# Normalize, apply colormap to heatmap and combine with original image
heatmap_normalized = cv2.normalize(self.heatmap, None, 0, 255, cv2.NORM_MINMAX)
heatmap_colored = cv2.applyColorMap(heatmap_normalized.astype(np.uint8), self.colormap)
if self.count_reg_pts is not None:
incount_label = 'InCount : ' + f'{self.in_counts}'
outcount_label = 'OutCount : ' + f'{self.out_counts}'
self.annotator.count_labels(in_count=incount_label, out_count=outcount_label)
im0_with_heatmap = cv2.addWeighted(self.im0, 1 - self.heatmap_alpha, heatmap_colored, self.heatmap_alpha, 0)
if self.view_img:
@ -94,6 +168,7 @@ class Heatmap:
im0_with_heatmap (nd array): Original Image with heatmap
"""
cv2.imshow('Ultralytics Heatmap', im0_with_heatmap)
if cv2.waitKey(1) & 0xFF == ord('q'):
return

3
ultralytics/solutions/object_counter.py
View File

@ -119,7 +119,8 @@ class ObjectCounter:
# Draw Tracks
track_line = self.track_history[track_id]
track_line.append((float((box[0] + box[2]) / 2), float((box[1] + box[3]) / 2)))
track_line.pop(0) if len(track_line) > 30 else None
if len(track_line) > 30:
track_line.pop(0)
if self.draw_tracks:
self.annotator.draw_centroid_and_tracks(track_line,

4
ultralytics/utils/plotting.py
View File

@ -259,9 +259,9 @@ class Annotator:
return np.asarray(self.im)
# Object Counting Annotator
def draw_region(self, reg_pts=None, color=(0, 255, 0)):
def draw_region(self, reg_pts=None, color=(0, 255, 0), thickness=5):
# Draw region line
cv2.polylines(self.im, [np.array(reg_pts, dtype=np.int32)], isClosed=True, color=color, thickness=self.tf + 2)
cv2.polylines(self.im, [np.array(reg_pts, dtype=np.int32)], isClosed=True, color=color, thickness=thickness)
def draw_centroid_and_tracks(self, track, color=(255, 0, 255), track_thickness=2):
# Draw region line