ultralytics 8.0.41 TF SavedModel and EdgeTPU export (ultralytics#1034)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Noobtoss <[email protected]> Co-authored-by: Ayush Chaurasia <[email protected]>
CV4EcologySchool · Feb 20, 2023 · f6e393c · f6e393c
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diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml
@@ -17,7 +17,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        os: [ubuntu-latest]
+        os: [ubuntu-latest, windows-latest, macos-latest]
         python-version: ['3.10']  # requires python<=3.9
         model: [yolov8n]
     steps:
@@ -34,7 +34,8 @@ jobs:
       - name: Install requirements
         run: |
           python -m pip install --upgrade pip wheel
-          pip install -e '.[export]' --extra-index-url https://download.pytorch.org/whl/cpu
+          pip install -e . coremltools openvino-dev tensorflow-cpu paddlepaddle x2paddle --extra-index-url https://download.pytorch.org/whl/cpu
+          yolo export format=tflite
       - name: Check environment
         run: |
           echo "RUNNER_OS is ${{ runner.os }}"
@@ -46,37 +47,29 @@ jobs:
           python --version
           pip --version
           pip list
-      - name: TF Lite export
-        run: |
-          yolo export model=${{ matrix.model }}.pt format=tflite
-          yolo task=detect mode=predict model=yolov8n_saved_model/yolov8n_float16.tflite imgsz=640
-      - name: TF *.pb export
-        run: |
-          yolo export model=${{ matrix.model }}.pt format=pb
-          yolo task=detect mode=predict model=yolov8n.pb imgsz=640
-      - name: TF Lite Edge TPU export
-        run: |
-          yolo export model=${{ matrix.model }}.pt format=edgetpu
-      - name: TF.js export
-        run: |
-          yolo export model=${{ matrix.model }}.pt format=tfjs
       - name: Benchmark DetectionModel
+        shell: python
         run: |
-          # yolo benchmark model=${{ matrix.model }}.pt imgsz=320 min_metric=0.29
+          from ultralytics.yolo.utils.benchmarks import run_benchmarks
+          run_benchmarks(model='yolov8n.pt', imgsz=160, half=False, hard_fail=False)
       - name: Benchmark SegmentationModel
+        shell: python
         run: |
-          # yolo benchmark model=${{ matrix.model }}-seg.pt imgsz=320 min_metric=0.29
+          from ultralytics.yolo.utils.benchmarks import run_benchmarks
+          run_benchmarks(model='yolov8n-seg.pt', imgsz=160, half=False, hard_fail=False)
       - name: Benchmark ClassificationModel
+        shell: python
         run: |
-          # yolo benchmark model=${{ matrix.model }}-cls.pt imgsz=224 min_metric=0.29
+          from ultralytics.yolo.utils.benchmarks import run_benchmarks
+          run_benchmarks(model='yolov8n-cls.pt', imgsz=160, half=False, hard_fail=False)
 
   Tests:
     timeout-minutes: 60
     runs-on: ${{ matrix.os }}
     strategy:
       fail-fast: false
       matrix:
-        os: [ubuntu-latest, windows-latest, macos-latest]
+        os: [ubuntu-latest]
         python-version: ['3.10']
         model: [yolov8n]
         torch: [latest]
@@ -155,9 +148,9 @@ jobs:
       - name: Test classification
         shell: bash  # for Windows compatibility
         run: |
-          yolo task=classify mode=train data=mnist160 model=yolov8n-cls.yaml epochs=1 imgsz=32
-          yolo task=classify mode=train data=mnist160 model=yolov8n-cls.pt epochs=1 imgsz=32
-          yolo task=classify mode=val data=mnist160 model=runs/classify/train/weights/last.pt imgsz=32
+          yolo task=classify mode=train data=imagenet10 model=yolov8n-cls.yaml epochs=1 imgsz=32
+          yolo task=classify mode=train data=imagenet10 model=yolov8n-cls.pt epochs=1 imgsz=32
+          yolo task=classify mode=val data=imagenet10 model=runs/classify/train/weights/last.pt imgsz=32
           yolo task=classify mode=predict model=runs/classify/train/weights/last.pt imgsz=32 source=ultralytics/assets/bus.jpg
           yolo mode=export model=runs/classify/train/weights/last.pt imgsz=32 format=torchscript
       - name: Pytest tests

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
@@ -59,7 +59,9 @@ To allow your work to be integrated as seamlessly as possible, we advise you to:
 
 ### 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
 """

diff --git a/README.md b/README.md
@@ -222,7 +222,10 @@ See [Classification Docs](https://docs.ultralytics.com/tasks/classification/) fo
 
 ## <div align="center">Ultralytics HUB</div>
 
-Experience seamless AI with [Ultralytics HUB](https://bit.ly/ultralytics_hub) ⭐, the all-in-one solution for data visualization, YOLOv5 and YOLOv8 (coming soon) 🚀 model training and deployment, without any coding. Transform images into actionable insights and bring your AI visions to life with ease using our cutting-edge platform and user-friendly [Ultralytics App](https://ultralytics.com/app_install). Start your journey for **Free** now!
+Experience seamless AI with [Ultralytics HUB](https://bit.ly/ultralytics_hub) ⭐, the all-in-one solution for data
+visualization, YOLOv5 and YOLOv8 (coming soon) 🚀 model training and deployment, without any coding. Transform images
+into actionable insights and bring your AI visions to life with ease using our cutting-edge platform and
+user-friendly [Ultralytics App](https://ultralytics.com/app_install). Start your journey for **Free** now!
 
 <a href="https://bit.ly/ultralytics_hub" target="_blank">
 <img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png"></a>
@@ -250,7 +253,9 @@ YOLOv8 is available under two different licenses:
 
 ## <div align="center">Contact</div>
 
-For YOLOv8 bug reports and feature requests please visit [GitHub Issues](https://github.com/ultralytics/ultralytics/issues) or the [Ultralytics Community Forum](https://community.ultralytics.com/).
+For YOLOv8 bug reports and feature requests please
+visit [GitHub Issues](https://github.com/ultralytics/ultralytics/issues) or
+the [Ultralytics Community Forum](https://community.ultralytics.com/).
 
 <br>
 <div align="center">

diff --git a/README.zh-CN.md b/README.zh-CN.md
@@ -18,7 +18,9 @@
   </div>
   <br>
 
-[Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) 是由 [Ultralytics](https://ultralytics.com) 开发的一个前沿的 SOTA 模型。它在以前成功的 YOLO 版本基础上，引入了新的功能和改进，进一步提升了其性能和灵活性。YOLOv8 基于快速、准确和易于使用的设计理念，使其成为广泛的目标检测、图像分割和图像分类任务的绝佳选择。
+[Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) 是由 [Ultralytics](https://ultralytics.com) 开发的一个前沿的
+SOTA 模型。它在以前成功的 YOLO 版本基础上，引入了新的功能和改进，进一步提升了其性能和灵活性。YOLOv8
+基于快速、准确和易于使用的设计理念，使其成为广泛的目标检测、图像分割和图像分类任务的绝佳选择。
 
 如果要申请企业许可证，请填写 [Ultralytics 许可](https://ultralytics.com/license)。
 
@@ -53,7 +55,9 @@
 <details open>
 <summary>安装</summary>
 
-Pip 安装包含所有 [requirements.txt](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt) 的 ultralytics 包，环境要求 [**Python>=3.7**](https://www.python.org/)，且 [**PyTorch>=1.7**](https://pytorch.org/get-started/locally/)。
+Pip 安装包含所有 [requirements.txt](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt) 的
+ultralytics 包，环境要求 [**Python>=3.7**](https://www.python.org/)，且 [\*\*PyTorch>=1.7
+\*\*](https://pytorch.org/get-started/locally/)。
 
 ```bash
 pip install ultralytics
@@ -70,7 +74,8 @@ YOLOv8 可以直接在命令行界面（CLI）中使用 `yolo` 命令运行：
 yolo predict model=yolov8n.pt source="https://ultralytics.com/images/bus.jpg"
 ```
 
-`yolo`可以用于各种任务和模式，并接受额外的参数，例如 `imgsz=640`。参见 YOLOv8 [文档](https://docs.ultralytics.com)中可用`yolo`[参数](https://docs.ultralytics.com/cfg/)的完整列表。
+`yolo`可以用于各种任务和模式，并接受额外的参数，例如 `imgsz=640`。参见 YOLOv8 [文档](https://docs.ultralytics.com)
+中可用`yolo`[参数](https://docs.ultralytics.com/cfg/)的完整列表。
 
 ```bash
 yolo task=detect    mode=train    model=yolov8n.pt        args...
@@ -95,11 +100,13 @@ results = model("https://ultralytics.com/images/bus.jpg")  # 预测图像
 success = model.export(format="onnx")  # 将模型导出为 ONNX 格式
 ```
 
-[模型](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) 会从 Ultralytics [发布页](https://github.com/ultralytics/ultralytics/releases) 自动下载。
+[模型](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) 会从
+Ultralytics [发布页](https://github.com/ultralytics/ultralytics/releases) 自动下载。
 
 ### 已知问题 / 待办事项
 
-我们仍在努力完善 YOLOv8 的几个部分！我们的目标是尽快完成这些工作，使 YOLOv8 的功能设置达到YOLOv5 的水平，包括对所有相同格式的导出和推理。我们还在写一篇 YOLOv8 的论文，一旦完成，我们将提交给 [arxiv.org](https://arxiv.org)。
+我们仍在努力完善 YOLOv8 的几个部分！我们的目标是尽快完成这些工作，使 YOLOv8 的功能设置达到YOLOv5
+的水平，包括对所有相同格式的导出和推理。我们还在写一篇 YOLOv8 的论文，一旦完成，我们将提交给 [arxiv.org](https://arxiv.org)。
 
 - [x] TensorFlow 导出
 - [x] DDP 恢复训练
@@ -111,7 +118,8 @@ success = model.export(format="onnx")  # 将模型导出为 ONNX 格式
 
 所有 YOLOv8 的预训练模型都可以在这里找到。目标检测和分割模型是在 COCO 数据集上预训练的，而分类模型是在 ImageNet 数据集上预训练的。
 
-第一次使用时，[模型](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) 会从 Ultralytics [发布页](https://github.com/ultralytics/ultralytics/releases) 自动下载。
+第一次使用时，[模型](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) 会从
+Ultralytics [发布页](https://github.com/ultralytics/ultralytics/releases) 自动下载。
 
 <details open><summary>目标检测</summary>
 
@@ -125,7 +133,8 @@ success = model.export(format="onnx")  # 将模型导出为 ONNX 格式
 
 - **mAP<sup>val</sup>** 结果都在 [COCO val2017](http://cocodataset.org) 数据集上，使用单模型单尺度测试得到。
   <br>复现命令 `yolo val detect data=coco.yaml device=0`
-- **推理速度**使用 COCO 验证集图片推理时间进行平均得到，测试环境使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例。
+- **推理速度**使用 COCO
+  验证集图片推理时间进行平均得到，测试环境使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例。
   <br>复现命令 `yolo val detect data=coco128.yaml batch=1 device=0/cpu`
 
 </details>
@@ -142,7 +151,8 @@ success = model.export(format="onnx")  # 将模型导出为 ONNX 格式
 
 - **mAP<sup>val</sup>**  结果都在 [COCO val2017](http://cocodataset.org) 数据集上，使用单模型单尺度测试得到。
   <br>复现命令 `yolo val segment data=coco.yaml device=0`
-- **推理速度**使用 COCO 验证集图片推理时间进行平均得到，测试环境使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例。
+- **推理速度**使用 COCO
+  验证集图片推理时间进行平均得到，测试环境使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例。
   <br>复现命令 `yolo val segment data=coco128-seg.yaml batch=1 device=0/cpu`
 
 </details>
@@ -159,7 +169,8 @@ success = model.export(format="onnx")  # 将模型导出为 ONNX 格式
 
 - **acc** 都在 [ImageNet](https://www.image-net.org/) 数据集上，使用单模型单尺度测试得到。
   <br>复现命令 `yolo val classify data=path/to/ImageNet device=0`
-- **推理速度**使用 ImageNet 验证集图片推理时间进行平均得到，测试环境使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例。
+- **推理速度**使用 ImageNet
+  验证集图片推理时间进行平均得到，测试环境使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例。
   <br>复现命令 `yolo val classify data=path/to/ImageNet batch=1 device=0/cpu`
 
 </details>
@@ -192,14 +203,18 @@ success = model.export(format="onnx")  # 将模型导出为 ONNX 格式
 
 ## <div align="center">Ultralytics HUB</div>
 
-[Ultralytics HUB](https://bit.ly/ultralytics_hub) 是我们⭐ **新**的无代码解决方案，用于可视化数据集，训练 YOLOv8🚀 模型，并以无缝体验方式部署到现实世界。现在开始**免费**! 还可以通过下载 [Ultralytics App](https://ultralytics.com/app_install) 在你的 iOS 或 Android 设备上运行 YOLOv8 模型!
+[Ultralytics HUB](https://bit.ly/ultralytics_hub) 是我们⭐ **新**的无代码解决方案，用于可视化数据集，训练 YOLOv8🚀
+模型，并以无缝体验方式部署到现实世界。现在开始**免费**!
+还可以通过下载 [Ultralytics App](https://ultralytics.com/app_install) 在你的 iOS 或 Android 设备上运行 YOLOv8 模型!
 
 <a href="https://bit.ly/ultralytics_hub" target="_blank">
 <img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png"></a>
 
 ## <div align="center">贡献</div>
 
-我们喜欢您的意见或建议！我们希望尽可能简单和透明地为 YOLOv8 做出贡献。请看我们的 [贡献指南](CONTRIBUTING.md) ，并填写 [调查问卷](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) 向我们发送您的体验反馈。感谢我们所有的贡献者！
+我们喜欢您的意见或建议！我们希望尽可能简单和透明地为 YOLOv8 做出贡献。请看我们的 [贡献指南](CONTRIBUTING.md)
+，并填写 [调查问卷](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey)
+向我们发送您的体验反馈。感谢我们所有的贡献者！
 
 <!-- SVG image from https://opencollective.com/ultralytics/contributors.svg?width=990 -->
 
@@ -210,11 +225,13 @@ success = model.export(format="onnx")  # 将模型导出为 ONNX 格式
 
 - YOLOv8 在两种不同的 License 下可用：
   - **GPL-3.0 License**： 查看 [License](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) 文件的详细信息。
-  - **企业License**：在没有 GPL-3.0 开源要求的情况下为商业产品开发提供更大的灵活性。典型用例是将 Ultralytics 软件和 AI 模型嵌入到商业产品和应用程序中。在以下位置申请企业许可证 [Ultralytics 许可](https://ultralytics.com/license) 。
+  - **企业License**：在没有 GPL-3.0 开源要求的情况下为商业产品开发提供更大的灵活性。典型用例是将 Ultralytics 软件和 AI
+    模型嵌入到商业产品和应用程序中。在以下位置申请企业许可证 [Ultralytics 许可](https://ultralytics.com/license) 。
 
 ## <div align="center">联系我们</div>
 
-请访问 [GitHub Issues](https://github.com/ultralytics/ultralytics/issues) 或 [Ultralytics Community Forum](https://community.ultralytis.com) 以报告 YOLOv8 错误和请求功能。
+请访问 [GitHub Issues](https://github.com/ultralytics/ultralytics/issues)
+或 [Ultralytics Community Forum](https://community.ultralytis.com) 以报告 YOLOv8 错误和请求功能。
 
 <br>
 <div align="center">

diff --git a/docker/Dockerfile b/docker/Dockerfile
@@ -13,7 +13,7 @@ ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Aria
 ENV DEBIAN_FRONTEND noninteractive
 RUN apt update
 RUN TZ=Etc/UTC apt install -y tzdata
-RUN apt install --no-install-recommends -y gcc git zip curl htop libgl1-mesa-glx libglib2.0-0 libpython3-dev gnupg
+RUN apt install --no-install-recommends -y gcc git zip curl htop libgl1-mesa-glx libglib2.0-0 libpython3-dev gnupg g++
 # RUN alias python=python3
 
 # Security updates

diff --git a/docker/Dockerfile-cpu b/docker/Dockerfile-cpu
@@ -12,7 +12,7 @@ ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Aria
 ENV DEBIAN_FRONTEND noninteractive
 RUN apt update
 RUN TZ=Etc/UTC apt install -y tzdata
-RUN apt install --no-install-recommends -y python3-pip git zip curl htop libgl1-mesa-glx libglib2.0-0 libpython3-dev gnupg
+RUN apt install --no-install-recommends -y python3-pip git zip curl htop libgl1-mesa-glx libglib2.0-0 libpython3-dev gnupg g++
 # RUN alias python=python3
 
 # Create working directory

diff --git a/docs/SECURITY.md b/docs/SECURITY.md
@@ -1,17 +1,26 @@
-At [Ultralytics](https://ultralytics.com), the security of our users' data and systems is of utmost importance. To ensure the safety and security of our [open-source projects](https://github.com/ultralytics), we have implemented several measures to detect and prevent security vulnerabilities.
+At [Ultralytics](https://ultralytics.com), the security of our users' data and systems is of utmost importance. To
+ensure the safety and security of our [open-source projects](https://github.com/ultralytics), we have implemented
+several measures to detect and prevent security vulnerabilities.
 
 [![ultralytics](https://snyk.io/advisor/python/ultralytics/badge.svg)](https://snyk.io/advisor/python/ultralytics)
 
 ## Snyk Scanning
 
-We use [Snyk](https://snyk.io/advisor/python/ultralytics) to regularly scan the YOLOv8 repository for vulnerabilities and security issues. Our goal is to identify and remediate any potential threats as soon as possible, to minimize any risks to our users.
+We use [Snyk](https://snyk.io/advisor/python/ultralytics) to regularly scan the YOLOv8 repository for vulnerabilities
+and security issues. Our goal is to identify and remediate any potential threats as soon as possible, to minimize any
+risks to our users.
 
 ## GitHub CodeQL Scanning
 
-In addition to our Snyk scans, we also use GitHub's [CodeQL](https://docs.github.com/en/code-security/code-scanning/automatically-scanning-your-code-for-vulnerabilities-and-errors/about-code-scanning-with-codeql) scans to proactively identify and address security vulnerabilities.
+In addition to our Snyk scans, we also use
+GitHub's [CodeQL](https://docs.github.com/en/code-security/code-scanning/automatically-scanning-your-code-for-vulnerabilities-and-errors/about-code-scanning-with-codeql)
+scans to proactively identify and address security vulnerabilities.
 
 ## Reporting Security Issues
 
-If you suspect or discover a security vulnerability in the YOLOv8 repository, please let us know immediately. You can reach out to us directly via our [contact form](https://ultralytics.com/contact) or via [[email protected]](mailto:[email protected]). Our security team will investigate and respond as soon as possible.
+If you suspect or discover a security vulnerability in the YOLOv8 repository, please let us know immediately. You can
+reach out to us directly via our [contact form](https://ultralytics.com/contact) or
+via [[email protected]](mailto:[email protected]). Our security team will investigate and respond as soon
+as possible.
 
 We appreciate your help in keeping the YOLOv8 repository secure and safe for everyone.
diff --git a/docs/app.md b/docs/app.md
@@ -35,7 +35,7 @@
 <br>
 
 Welcome to the Ultralytics HUB app for demonstrating YOLOv5 and YOLOv8 models! In this app, available on the [Apple App
-Store](https://apps.apple.com/xk/app/ultralytics/id1583935240) and the 
+Store](https://apps.apple.com/xk/app/ultralytics/id1583935240) and the
 [Google Play Store](https://play.google.com/store/apps/details?id=com.ultralytics.ultralytics_app), you will be able
 to see the power and capabilities of YOLOv5, a state-of-the-art object detection model developed by Ultralytics.