ultralytics 8.2.25 latest TensorFlow 2.16 support (#13176)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: lakshanthad <lakshanthad@yahoo.com>
This commit is contained in:
Glenn Jocher
GitHub
parent
d80bd840d1
commit
dd819fc420
9 changed files with 79 additions and 49 deletions
10
.github/workflows/ci.yaml
vendored
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.github/workflows/ci.yaml
vendored
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@ -206,11 +206,16 @@ jobs:
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run: |
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# CoreML must be installed before export due to protobuf error from AutoInstall
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python -m pip install --upgrade pip wheel
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slow=""
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torch=""
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if [ "${{ matrix.torch }}" == "1.8.0" ]; then
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torch="torch==1.8.0 torchvision==0.9.0"
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fi
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pip install -e . $torch pytest-cov "coremltools>=7.0; platform_system != 'Windows' and python_version <= '3.11'" --extra-index-url https://download.pytorch.org/whl/cpu
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if [[ "${{ github.event_name }}" =~ ^(schedule|workflow_dispatch)$ ]]; then
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slow="pycocotools mlflow ray[tune]"
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fi
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slow="pycocotools mlflow ray[tune]"
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pip install -e ".[export]" $torch $slow pytest-cov --extra-index-url https://download.pytorch.org/whl/cpu
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- name: Check environment
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run: |
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yolo checks
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@ -220,7 +225,6 @@ jobs:
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run: |
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slow=""
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if [[ "${{ github.event_name }}" =~ ^(schedule|workflow_dispatch)$ ]]; then
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pip install pycocotools mlflow "ray[tune]"
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slow="--slow"
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fi
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pytest $slow --cov=ultralytics/ --cov-report xml tests/
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@ -272,7 +276,7 @@ jobs:
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- name: Install requirements
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run: |
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python -m pip install --upgrade pip wheel
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pip install --no-cache-dir -e ".[export]" pytest mlflow pycocotools "ray[tune]"
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pip install -e ".[export]" pytest mlflow pycocotools "ray[tune]"
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- name: Check environment
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run: |
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yolo checks
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@ -16,14 +16,14 @@ This guide provides a comprehensive overview of three fundamental types of data
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|:------------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------:|
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|  |  |  |
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### Why Graphs are Important
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### Why Graphs are Important
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- Line graphs are ideal for tracking changes over short and long periods and for comparing changes for multiple groups over the same period.
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- Bar plots, on the other hand, are suitable for comparing quantities across different categories and showing relationships between a category and its numerical value.
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- Line graphs are ideal for tracking changes over short and long periods and for comparing changes for multiple groups over the same period.
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- Bar plots, on the other hand, are suitable for comparing quantities across different categories and showing relationships between a category and its numerical value.
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- Lastly, pie charts are effective for illustrating proportions among categories and showing parts of a whole.
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!!! Analytics "Analytics Examples"
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=== "Line Graph"
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```python
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@ -174,21 +174,21 @@ This guide provides a comprehensive overview of three fundamental types of data
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Here's a table with the `Analytics` arguments:
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| Name | Type | Default | Description |
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|--------------|-------------------|---------------|---------------------------------------------|
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| `type` | `str` | `None` | Type of data or object. |
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| `im0_shape` | `tuple` | `None` | Shape of the initial image. |
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| `writer` | `cv2.VideoWriter` | `None` | Object for writing video files. |
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| `title` | `str` | `ultralytics` | Title for the visualization. |
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| `x_label` | `str` | `x` | Label for the x-axis. |
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| `y_label` | `str` | `y` | Label for the y-axis. |
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| `bg_color` | `str` | `white` | Background color. |
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| `fg_color` | `str` | `black` | Foreground color. |
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| `line_color` | `str` | `yellow` | Color of the lines. |
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| `line_width` | `int` | `2` | Width of the lines. |
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| `fontsize` | `int` | `13` | Font size for text. |
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| `view_img` | `bool` | `False` | Flag to display the image or video. |
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| `save_img` | `bool` | `True` | Flag to save the image or video. |
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| Name | Type | Default | Description |
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|--------------|-------------------|---------------|-------------------------------------|
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| `type` | `str` | `None` | Type of data or object. |
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| `im0_shape` | `tuple` | `None` | Shape of the initial image. |
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| `writer` | `cv2.VideoWriter` | `None` | Object for writing video files. |
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| `title` | `str` | `ultralytics` | Title for the visualization. |
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| `x_label` | `str` | `x` | Label for the x-axis. |
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| `y_label` | `str` | `y` | Label for the y-axis. |
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| `bg_color` | `str` | `white` | Background color. |
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| `fg_color` | `str` | `black` | Foreground color. |
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| `line_color` | `str` | `yellow` | Color of the lines. |
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| `line_width` | `int` | `2` | Width of the lines. |
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| `fontsize` | `int` | `13` | Font size for text. |
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| `view_img` | `bool` | `False` | Flag to display the image or video. |
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| `save_img` | `bool` | `True` | Flag to save the image or video. |
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### Arguments `model.track`
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@ -37,12 +37,12 @@ Primary users include traffic management authorities and law enforcement, while
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### Setting Measurable Objectives
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Setting measurable objectives is key to the success of a computer vision project. These goals should be clear, achievable, and time-bound.
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Setting measurable objectives is key to the success of a computer vision project. These goals should be clear, achievable, and time-bound.
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For example, if you are developing a system to estimate vehicle speeds on a highway. You could consider the following measurable objectives:
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- To achieve at least 95% accuracy in speed detection within six months, using a dataset of 10,000 vehicle images.
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- The system should be able to process real-time video feeds at 30 frames per second with minimal delay.
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- To achieve at least 95% accuracy in speed detection within six months, using a dataset of 10,000 vehicle images.
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- The system should be able to process real-time video feeds at 30 frames per second with minimal delay.
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By setting specific and quantifiable goals, you can effectively track progress, identify areas for improvement, and ensure the project stays on course.
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@ -50,7 +50,7 @@ By setting specific and quantifiable goals, you can effectively track progress,
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Your problem statement helps you conceptualize which computer vision task can solve your issue.
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For example, if your problem is monitoring vehicle speeds on a highway, the relevant computer vision task is object tracking. [Object tracking](../modes/track.md) is suitable because it allows the system to continuously follow each vehicle in the video feed, which is crucial for accurately calculating their speeds.
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For example, if your problem is monitoring vehicle speeds on a highway, the relevant computer vision task is object tracking. [Object tracking](../modes/track.md) is suitable because it allows the system to continuously follow each vehicle in the video feed, which is crucial for accurately calculating their speeds.
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<p align="center">
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<img width="100%" src="https://assets-global.website-files.com/6479eab6eb2ed5e597810e9e/664f03ba300cf6e61689862f_FIG%20444.gif" alt="Example of Object Tracking">
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@ -134,7 +134,7 @@ Connecting with other computer vision enthusiasts can be incredibly helpful for
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### Comprehensive Guides and Documentation
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- **Ultralytics YOLOv8 Documentation:** Explore the [official YOLOv8 documentation](./index.md) for in-depth guides and valuable tips on various computer vision tasks and projects.
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- **Ultralytics YOLOv8 Documentation:** Explore the [official YOLOv8 documentation](./index.md) for in-depth guides and valuable tips on various computer vision tasks and projects.
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## Conclusion
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@ -8,7 +8,7 @@ keywords: Computer Vision Steps, How Does Computer Vision Work, Computer Vision
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## Introduction
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Computer vision is a subfield of artificial intelligence (AI) that helps computers see and understand the world like humans do. It processes and analyzes images or videos to extract information, recognize patterns, and make decisions based on that data.
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Computer vision is a subfield of artificial intelligence (AI) that helps computers see and understand the world like humans do. It processes and analyzes images or videos to extract information, recognize patterns, and make decisions based on that data.
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Computer vision techniques like [object detection](../tasks/detect.md), [image classification](../tasks/classify.md), and [instance segmentation](../tasks/segment.md) can be applied across various industries, from [autonomous driving](https://www.ultralytics.com/solutions/ai-in-self-driving) to [medical imaging](https://www.ultralytics.com/solutions/ai-in-healthcare), to gain valuable insights.
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@ -16,7 +16,7 @@ Computer vision techniques like [object detection](../tasks/detect.md), [image c
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<img width="100%" src="https://media.licdn.com/dms/image/D4D12AQGf61lmNOm3xA/article-cover_image-shrink_720_1280/0/1656513646049?e=1722470400&v=beta&t=23Rqohhxfie38U5syPeL2XepV2QZe6_HSSC-4rAAvt4" alt="Overview of computer vision techniques">
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</p>
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Working on your own computer vision projects is a great way to understand and learn more about computer vision. However, a computer vision project can consist of many steps, and it might seem confusing at first. By the end of this guide, you’ll be familiar with the steps involved in a computer vision project. We’ll walk through everything from the beginning to the end of a project, explaining why each part is important. Let’s get started and make your computer vision project a success!
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Working on your own computer vision projects is a great way to understand and learn more about computer vision. However, a computer vision project can consist of many steps, and it might seem confusing at first. By the end of this guide, you’ll be familiar with the steps involved in a computer vision project. We’ll walk through everything from the beginning to the end of a project, explaining why each part is important. Let’s get started and make your computer vision project a success!
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## An Overview of a Computer Vision Project
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@ -36,7 +36,7 @@ Now that we know what to expect, let's dive right into the steps and get your pr
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## Step 1: Defining Your Project’s Goals
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The first step in any computer vision project is clearly defining the problem you’re trying to solve. Knowing the end goal helps you start to build a solution. This is especially true when it comes to computer vision because your project’s objective will directly affect which computer vision task you need to focus on.
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The first step in any computer vision project is clearly defining the problem you’re trying to solve. Knowing the end goal helps you start to build a solution. This is especially true when it comes to computer vision because your project’s objective will directly affect which computer vision task you need to focus on.
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Here are some examples of project objectives and the computer vision tasks that can be used to reach these objectives:
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@ -51,11 +51,11 @@ Here are some examples of project objectives and the computer vision tasks that
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### Step 1.5: Selecting the Right Model and Training Approach
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After understanding the project objective and suitable computer vision tasks, an essential part of defining the project goal is [selecting the right model](../models/index.md) and training approach.
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After understanding the project objective and suitable computer vision tasks, an essential part of defining the project goal is [selecting the right model](../models/index.md) and training approach.
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Depending on the objective, you might choose to select the model first or after seeing what data you are able to collect in Step 2. For example, suppose your project is highly dependent on the availability of specific types of data. In that case, it may be more practical to gather and analyze the data first before selecting a model. On the other hand, if you have a clear understanding of the model requirements, you can choose the model first and then collect data that fits those specifications.
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Choosing between training from scratch or using transfer learning affects how you prepare your data. Training from scratch requires a diverse dataset to build the model’s understanding from the ground up. Transfer learning, on the other hand, allows you to use a pre-trained model and adapt it with a smaller, more specific dataset. Also, choosing a specific model to train will determine how you need to prepare your data, such as resizing images or adding annotations, according to the model’s specific requirements.
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Choosing between training from scratch or using transfer learning affects how you prepare your data. Training from scratch requires a diverse dataset to build the model’s understanding from the ground up. Transfer learning, on the other hand, allows you to use a pre-trained model and adapt it with a smaller, more specific dataset. Also, choosing a specific model to train will determine how you need to prepare your data, such as resizing images or adding annotations, according to the model’s specific requirements.
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<p align="center">
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<img width="100%" src="https://miro.medium.com/v2/resize:fit:1330/format:webp/1*zCnoXfPVcdXizTmhL68Rlw.jpeg" alt="Training From Scratch Vs. Using Transfer Learning">
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@ -117,7 +117,7 @@ By properly understanding, splitting, and augmenting your data, you can develop
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## Step 4: Model Training
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Once your dataset is ready for training, you can focus on setting up the necessary environment, managing your datasets, and training your model.
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Once your dataset is ready for training, you can focus on setting up the necessary environment, managing your datasets, and training your model.
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First, you’ll need to make sure your environment is configured correctly. Typically, this includes the following:
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@ -141,7 +141,7 @@ It’s important to assess your model's performance using various metrics and re
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## Step 6: Model Testing
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In this step, you can make sure that your model performs well on completely unseen data, confirming its readiness for deployment. The difference between model testing and model evaluation is that it focuses on verifying the final model's performance rather than iteratively improving it.
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In this step, you can make sure that your model performs well on completely unseen data, confirming its readiness for deployment. The difference between model testing and model evaluation is that it focuses on verifying the final model's performance rather than iteratively improving it.
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It's important to thoroughly test and debug any common issues that may arise. Test your model on a separate test dataset that was not used during training or validation. This dataset should represent real-world scenarios to ensure the model's performance is consistent and reliable.
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@ -100,8 +100,9 @@ export = [
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"onnx>=1.12.0", # ONNX export
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"coremltools>=7.0; platform_system != 'Windows' and python_version <= '3.11'", # CoreML supported on macOS and Linux
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"openvino>=2024.0.0", # OpenVINO export
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"tensorflow<=2.13.1; python_version <= '3.11'", # TF bug https://github.com/ultralytics/ultralytics/issues/5161
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"tensorflowjs>=3.9.0; python_version <= '3.11'", # TF.js export, automatically installs tensorflow
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"tensorflow>=2.0.0", # TF bug https://github.com/ultralytics/ultralytics/issues/5161
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"tensorflowjs>=3.9.0", # TF.js export, automatically installs tensorflow
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"keras", # not installed auotomatically by tensorflow>=2.16
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"flatbuffers>=23.5.26,<100; platform_machine == 'aarch64'", # update old 'flatbuffers' included inside tensorflow package
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"numpy==1.23.5; platform_machine == 'aarch64'", # fix error: `np.bool` was a deprecated alias for the builtin `bool` when using TensorRT models on NVIDIA Jetson
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"h5py!=3.11.0; platform_machine == 'aarch64'", # fix h5py build issues due to missing aarch64 wheels in 3.11 release
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@ -111,7 +112,6 @@ explorer = [
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"duckdb<=0.9.2", # SQL queries, duckdb==0.10.0 bug https://github.com/ultralytics/ultralytics/pull/8181
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"streamlit", # visualizing with GUI
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]
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# tensorflow>=2.4.1,<=2.13.1 # TF exports (-cpu, -aarch64, -macos)
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# tflite-support # for TFLite model metadata
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# nvidia-pyindex # TensorRT export
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# nvidia-tensorrt # TensorRT export
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@ -129,6 +129,31 @@ def test_export_coreml_matrix(task, dynamic, int8, half, batch):
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shutil.rmtree(file) # cleanup
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@pytest.mark.slow
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@pytest.mark.skipif(not checks.IS_PYTHON_MINIMUM_3_10, reason="TFLite export requires Python>=3.10")
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@pytest.mark.skipif(not LINUX, reason="Test disabled as TF suffers from install conflicts on Windows and macOS")
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@pytest.mark.parametrize(
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"task, dynamic, int8, half, batch",
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[ # generate all combinations but exclude those where both int8 and half are True
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(task, dynamic, int8, half, batch)
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for task, dynamic, int8, half, batch in product(TASKS, [False], [True, False], [True, False], [1])
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if not (int8 and half) # exclude cases where both int8 and half are True
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],
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)
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def test_export_tflite_matrix(task, dynamic, int8, half, batch):
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"""Test YOLO exports to TFLite format."""
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file = YOLO(TASK2MODEL[task]).export(
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format="tflite",
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imgsz=32,
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dynamic=dynamic,
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int8=int8,
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half=half,
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batch=batch,
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)
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YOLO(file)([SOURCE] * batch, imgsz=32) # exported model inference at batch=3
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Path(file).unlink() # cleanup
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@pytest.mark.skipif(not TORCH_1_9, reason="CoreML>=7.2 not supported with PyTorch<=1.8")
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@pytest.mark.skipif(WINDOWS, reason="CoreML not supported on Windows") # RuntimeError: BlobWriter not loaded
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@pytest.mark.skipif(IS_RASPBERRYPI, reason="CoreML not supported on Raspberry Pi")
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@ -142,6 +167,7 @@ def test_export_coreml():
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YOLO(MODEL).export(format="coreml", nms=True, imgsz=32)
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@pytest.mark.skipif(not checks.IS_PYTHON_MINIMUM_3_10, reason="TFLite export requires Python>=3.10")
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@pytest.mark.skipif(not LINUX, reason="Test disabled as TF suffers from install conflicts on Windows and macOS")
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def test_export_tflite():
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"""
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@ -1,6 +1,6 @@
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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__version__ = "8.2.24"
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__version__ = "8.2.25"
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import os
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@ -83,7 +83,6 @@ from ultralytics.utils import (
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WINDOWS,
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__version__,
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callbacks,
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checks,
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colorstr,
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get_default_args,
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yaml_save,
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@ -813,15 +812,16 @@ class Exporter:
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import tensorflow as tf # noqa
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except ImportError:
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suffix = "-macos" if MACOS else "-aarch64" if ARM64 else "" if cuda else "-cpu"
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version = "" if ARM64 else "<=2.13.1"
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check_requirements((f"tensorflow{suffix}{version}", "keras"))
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version = ">=2.0.0"
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check_requirements(f"tensorflow{suffix}{version}")
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import tensorflow as tf # noqa
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if ARM64:
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check_requirements("cmake") # 'cmake' is needed to build onnxsim on aarch64
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check_requirements(
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(
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"keras",
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"onnx>=1.12.0",
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"onnx2tf>=1.15.4,<=1.17.5",
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"onnx2tf>1.17.5,<=1.22.3",
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"sng4onnx>=1.0.1",
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"onnxsim>=0.4.33",
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"onnx_graphsurgeon>=0.3.26",
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@ -835,7 +835,7 @@ class Exporter:
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LOGGER.info(f"\n{prefix} starting export with tensorflow {tf.__version__}...")
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check_version(
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tf.__version__,
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"<=2.13.1",
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">=2.0.0",
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name="tensorflow",
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verbose=True,
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msg="https://github.com/ultralytics/ultralytics/issues/5161",
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@ -88,14 +88,14 @@ def benchmark(
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emoji, filename = "❌", None # export defaults
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try:
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# Checks
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if i == 5: # CoreML
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assert not (IS_RASPBERRYPI or IS_JETSON), "CoreML export not supported on Raspberry Pi or NVIDIA Jetson"
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if i == 9: # Edge TPU
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assert LINUX and not ARM64, "Edge TPU export only supported on non-aarch64 Linux"
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elif i == 7: # TF GraphDef
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if i == 7: # TF GraphDef
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assert model.task != "obb", "TensorFlow GraphDef not supported for OBB task"
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elif i == 9: # Edge TPU
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assert LINUX and not ARM64, "Edge TPU export only supported on non-aarch64 Linux"
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elif i in {5, 10}: # CoreML and TF.js
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assert MACOS or LINUX, "export only supported on macOS and Linux"
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assert MACOS or LINUX, "CoreML and TF.js export only supported on macOS and Linux"
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assert not IS_RASPBERRYPI, "CoreML and TF.js export not supported on Raspberry Pi"
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assert not IS_JETSON, "CoreML and TF.js export not supported on NVIDIA Jetson"
|
||||
if i in {3, 5}: # CoreML and OpenVINO
|
||||
assert not IS_PYTHON_3_12, "CoreML and OpenVINO not supported on Python 3.12"
|
||||
if i in {6, 7, 8, 9, 10}: # All TF formats
|
||||
|
|
|
|||
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Add table
Add a link
Reference in a new issue