Docs: HUB Updates (#12804)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This commit is contained in:
Sergiu Waxmann
GitHub
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---
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comments: true
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description: Learn about the Ultralytics Android App, enabling real-time object detection using YOLO models. Discover in-app features, quantization methods, and delegate options for optimal performance.
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keywords: Ultralytics, Android App, real-time object detection, YOLO models, TensorFlow Lite, FP16 quantization, INT8 quantization, CPU, GPU, Hexagon, NNAPI
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description: Experience rapid object detection on your Android device with the Ultralytics YOLO model app. Click to learn more!.
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keywords: Ultralytics, YOLO, Android App, real-time object detection, TensorFlow Lite, hardware acceleration, FP16, INT8, GPU
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---
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# Ultralytics Android App: Real-time Object Detection with YOLO Models
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@ -97,4 +97,4 @@ To get started with the Ultralytics Android App, follow these steps:
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6. Explore the app's settings to adjust the detection threshold, enable or disable specific object classes, and more.
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With the Ultralytics Android App, you now have the power of real-time object detection using YOLO models right at your fingertips. Enjoy exploring the app's features and optimizing its settings to suit your specific use cases.
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With the Ultralytics Android App, you now have the power of real-time object detection using YOLO models right at your fingertips. Enjoy exploring the app's features and optimizing its settings to suit your specific use cases.
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@ -1,7 +1,7 @@
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---
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comments: true
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description: Explore the Ultralytics HUB App, offering the ability to run YOLOv5 and YOLOv8 models on your iOS and Android devices with optimized performance.
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keywords: Ultralytics, HUB App, YOLOv5, YOLOv8, mobile AI, real-time object detection, image recognition, mobile device, hardware acceleration, Apple Neural Engine, Android GPU, NNAPI, custom model training
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description: Unlock the power of YOLO models on iOS & Android with the Ultralytics HUB App. Experience optimized performance on-the-go!.
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keywords: Ultralytics HUB App, YOLOv5, YOLOv8, mobile object detection, iOS, Android, real-time image recognition
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---
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# Ultralytics HUB App
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@ -45,4 +45,4 @@ Welcome to the Ultralytics HUB App! We are excited to introduce this powerful mo
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- [**iOS**](ios.md): Learn about YOLO CoreML models accelerated on Apple's Neural Engine for iPhones and iPads.
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- [**Android**](android.md): Explore TFLite acceleration on Android mobile devices.
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Get started today by downloading the Ultralytics HUB App on your mobile device and unlock the potential of YOLOv5 and YOLOv8 models on-the-go. Don't forget to check out our comprehensive [HUB Docs](../index.md) for more information on training, deploying, and using your custom models with the Ultralytics HUB platform.
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Get started today by downloading the Ultralytics HUB App on your mobile device and unlock the potential of YOLOv5 and YOLOv8 models on-the-go. Don't forget to check out our comprehensive [HUB Docs](../index.md) for more information on training, deploying, and using your custom models with the Ultralytics HUB platform.
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@ -1,7 +1,7 @@
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---
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comments: true
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description: Execute object detection in real-time on your iOS devices utilizing YOLO models. Leverage the power of the Apple Neural Engine and Core ML for fast and efficient object detection.
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keywords: Ultralytics, iOS app, object detection, YOLO models, real time, Apple Neural Engine, Core ML, FP16, INT8, quantization
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description: Transform your iOS device into a powerful object detection tool with the Ultralytics iOS App, powered by YOLO models.
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keywords: Ultralytics, iOS app, YOLO, object detection, real-time, iPhone, iPad, quantization, Apple Neural Engine, Core ML
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---
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# Ultralytics iOS App: Real-time Object Detection with YOLO Models
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@ -87,4 +87,4 @@ To get started with the Ultralytics iOS App, follow these steps:
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6. Explore the app's settings to adjust the detection threshold, enable or disable specific object classes, and more.
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With the Ultralytics iOS App, you can now leverage the power of YOLO models for real-time object detection on your iPhone or iPad, powered by the Apple Neural Engine and optimized with FP16 or INT8 quantization.
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With the Ultralytics iOS App, you can now leverage the power of YOLO models for real-time object detection on your iPhone or iPad, powered by the Apple Neural Engine and optimized with FP16 or INT8 quantization.
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---
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comments: true
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description: Learn how to use Ultralytics HUB for efficient and user-friendly AI model training in the cloud. Follow our detailed guide for easy model creation, training, evaluation, and deployment.
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keywords: Ultralytics, HUB Models, AI model training, model creation, model training, model evaluation, model deployment
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description: Unlock one-click cloud training for your models on Ultralytics HUB Pro. Streamline your AI workflow today!.
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keywords: Ultralytics HUB, cloud training, AI model training, Pro users, easy model training, Ultralytics cloud, AI workflow
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---
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# Cloud Training
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# Ultralytics HUB Cloud Training
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[Ultralytics HUB](https://hub.ultralytics.com/) provides a powerful and user-friendly cloud platform to train custom object detection models. Easily select your dataset and the desired training method, then kick off the process with just a few clicks. Ultralytics HUB offers pre-built options and various model architectures to streamline your workflow.
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We've listened to the high demand and widespread interest and are thrilled to unveil [Ultralytics HUB](https://bit.ly/ultralytics_hub) Cloud Training, offering a single-click training experience for our [Pro](./pro.md) users!
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Read more about creating and other details of a Model at our [HUB Models page](models.md)
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[Ultralytics HUB](https://bit.ly/ultralytics_hub) [Pro](./pro.md) users can finetune [Ultralytics HUB](https://bit.ly/ultralytics_hub) models on a custom dataset using our Cloud Training solution, making the model training process easy. Say goodbye to complex setups and hello to streamlined workflows with [Ultralytics HUB](https://bit.ly/ultralytics_hub)'s intuitive interface.
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<p align="center">
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<br>
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<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/ie3vLUDNYZo"
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title="YouTube video player" frameborder="0"
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allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
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@ -23,49 +20,67 @@ Read more about creating and other details of a Model at our [HUB Models page](m
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<strong>Watch:</strong> New Feature 🌟 Introducing Ultralytics HUB Cloud Training
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</p>
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## Selecting an Instance
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## Train Model
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For details on picking a model and instances for it, please read our [Instances guide Page](models.md)
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In order to train models using Ultralytics Cloud Training, you need to [upgrade](./pro.md#upgrade) to the [Pro Plan](./pro.md).
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## Steps to Train the Model
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Follow the [Train Model](./models.md#train-model) instructions from the [Models](./models.md) page until you reach the third step ([Train](./models.md#3-train)) of the **Train Model** dialog. Once you are on this step, simply select the training duration (Epochs or Timed), the training instance, the payment method, and click the **Start Training** button. That's it!
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Once the instance has been selected, training a model using Ultralytics HUB is a three-step process, as below:
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1. Picking a Dataset - Read more about datasets, steps to add/remove datasets from the [Dataset page](datasets.md)
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2. Picking a Model - Read more about models, steps to create/share and handle a model on the [HUB Models page](models.md)
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3. Training the Model on the Chosen Dataset
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??? note "Note"
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Ultralytics HUB offers three training options:
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When you are on this step, you have the option to close the **Train Model** dialog and start training your model from the Model page later.
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- **Ultralytics Cloud** - Explained in this page.
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- **Google Colab** - Train on Google's popular Colab notebooks.
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- **Bring your own agent** - Train models locally on your own hardware or on-premise GPU servers.
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In order to start training your model, follow the instructions presented in these steps.
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Most of the times, you will use the Epochs training. The number of epochs can be adjusted on this step (if the training didn't start yet) and represents the number of times your dataset needs to go through the cycle of train, label, and test. The exact pricing based on the number of epochs is hard to determine, reason why we only allow the [Account Balance](./pro.md#account-balance) payment method.
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## Training via Ultralytics Cloud
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!!! note "Note"
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To start training your model using Ultralytics Cloud, simply select the Training Duration, Available Instances, and Payment options.
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When using the Epochs training, your [account balance](./pro.md#account-balance) needs to be at least US$5.00 to start training. In case you have a low balance, you can top-up directly from this step.
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**Training Duration** - Ultralytics offers two kinds of training durations:
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1. Training based on `Epochs`: This option allows you to train your model based on the number of times your dataset needs to go through the cycle of train, label, and test. The exact pricing based on the number of epochs is hard to determine. Hence, if the credit gets exhausted before the intended number of epochs, the training pauses, and you get a prompt to top-up and resume training.
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2. Timed Training: The timed training feature allows you to fix the time duration of the entire training process and also determines the estimated amount before the start of training.
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!!! note "Note"
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When using the Epochs training, the [account balance](./pro.md#account-balance) is deducted after every epoch.
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When the training starts, you can click **Done** and monitor the training progress on the Model page.
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Also, after every epoch, we check if you have enough [account balance](./pro.md#account-balance) for the next epoch. In case you don't have enough [account balance](./pro.md#account-balance) for the next epoch, we will stop the training session, allowing you to resume training your model from the last checkpoint saved.
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## Monitor Your Training
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Once the model and mode of training have been selected, you can monitor the training procedure on the `Train` section with the link provided in the terminal (on your agent/Google Colab) or a button from Ultralytics Cloud.
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Alternatively, you can use the Timed training. This option allows you to set the training duration. In this case, we can determine the exact pricing. You can pay upfront or using your [account balance](./pro.md#account-balance).
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If you have enough [account balance](./pro.md#account-balance), you can use the [Account Balance](./pro.md#account-balance) payment method.
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## Stopping and Resuming Your Training
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If you don't have enough [account balance](./pro.md#account-balance), you won't be able to use the [Account Balance](./pro.md#account-balance) payment method. You can pay upfront or top-up directly from this step.
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Before the training session starts, the initialization process spins up a dedicated instance equipped with GPU resources, which can sometimes take a while depending on the current demand and availability of GPU resources.
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!!! note "Note"
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The account balance is not deducted during the initialization process (before the training session starts).
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After the training session starts, you can monitor each step of the progress.
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If needed, you can stop the training by clicking on the **Stop Training** button.
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!!! note "Note"
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You can resume training your model from the last checkpoint saved.
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<p align="center">
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<br>
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<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/H3qL8ImCSV8"
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title="YouTube video player" frameborder="0"
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allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
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<strong>Watch:</strong> Pause and Resume Model Training Using Ultralytics HUB
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</p>
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Once the training has started, you can `Stop` the training, which will also correspondingly pause the credit usage. You can then `Resume` the training from the point where it stopped.
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!!! note "Note"
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Unfortunately, at the moment, you can only train one model at a time using Ultralytics Cloud.
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## Payments and Billing Options
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Ultralytics HUB offers `Pay Now` as upfront and/or using `Ultralytics HUB Account` as a wallet to top up and fulfill the billing. You can choose from two types of accounts: `Free` and `Pro` user.
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## Billing
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To access your profile, click on the profile picture in the bottom left corner.
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At any point during training or after training, you can check the cost of your model by clicking on the **Billing** tab. Furthermore, you can download a small cost report by clicking on the **Download** button.
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Click on the Billing tab to view your current plan and options to upgrade it.
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You will be prompted with different available plans, and you can pick from the available plans as shown below.
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Navigate to the Payment page, fill in the details, and complete the payment.
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@ -4,14 +4,13 @@ description: Learn how Ultralytics HUB datasets streamline your ML workflow. Upl
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keywords: Ultralytics, HUB datasets, YOLO model training, upload datasets, dataset validation, ML workflow, share datasets
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---
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# HUB Datasets
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# Ultralytics HUB Datasets
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[Ultralytics HUB](https://hub.ultralytics.com/) datasets are a practical solution for managing and leveraging your custom datasets.
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[Ultralytics HUB](https://bit.ly/ultralytics_hub) datasets are a practical solution for managing and leveraging your custom datasets.
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Once uploaded, datasets can be immediately utilized for model training. This integrated approach facilitates a seamless transition from dataset management to model training, significantly simplifying the entire process.
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<p align="center">
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<br>
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<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/R42s2zFtNIY"
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title="YouTube video player" frameborder="0"
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allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
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## Upload Dataset
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Ultralytics HUB datasets are just like YOLOv5 and YOLOv8 🚀 datasets. They use the same structure and the same label formats to keep everything simple.
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[Ultralytics HUB](https://bit.ly/ultralytics_hub) datasets are just like YOLOv5 and YOLOv8 🚀 datasets. They use the same structure and the same label formats to keep everything simple.
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Before you upload a dataset to Ultralytics HUB, make sure to **place your dataset YAML file inside the dataset root directory** and that **your dataset YAML, directory and ZIP have the same name**, as shown in the example below, and then zip the dataset directory.
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Before you upload a dataset to [Ultralytics HUB](https://bit.ly/ultralytics_hub), make sure to **place your dataset YAML file inside the dataset root directory** and that **your dataset YAML, directory and ZIP have the same name**, as shown in the example below, and then zip the dataset directory.
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For example, if your dataset is called "coco8", as our [COCO8](../datasets/detect/coco8.md) example dataset, then you should have a `coco8.yaml` inside your `coco8/` directory, which will create a `coco8.zip` when zipped:
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For example, if your dataset is called "coco8", as our [COCO8](https://docs.ultralytics.com/datasets/detect/coco8) example dataset, then you should have a `coco8.yaml` inside your `coco8/` directory, which will create a `coco8.zip` when zipped:
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```bash
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zip -r coco8.zip coco8
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You can download our [COCO8](https://github.com/ultralytics/hub/blob/main/example_datasets/coco8.zip) example dataset and unzip it to see exactly how to structure your dataset.
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<p align="center">
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<img src="https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/datasets/hub_upload_dataset_1.jpg" alt="COCO8 Dataset Structure" width="80%">
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<img src="https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/datasets/dataset_structure.jpg" alt="COCO8 Dataset Structure" width="80%">
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</p>
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The dataset YAML is the same standard YOLOv5 and YOLOv8 YAML format.
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--8<-- "ultralytics/cfg/datasets/coco8.yaml"
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```
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After zipping your dataset, you should validate it before uploading it to Ultralytics HUB. Ultralytics HUB conducts the dataset validation check post-upload, so by ensuring your dataset is correctly formatted and error-free ahead of time, you can forestall any setbacks due to dataset rejection.
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After zipping your dataset, you should [validate it](https://docs.ultralytics.com/reference/hub/__init__/#ultralytics.hub.check_dataset) before uploading it to [Ultralytics HUB](https://bit.ly/ultralytics_hub). [Ultralytics HUB](https://bit.ly/ultralytics_hub) conducts the dataset validation check post-upload, so by ensuring your dataset is correctly formatted and error-free ahead of time, you can forestall any setbacks due to dataset rejection.
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```py
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from ultralytics.hub import check_dataset
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check_dataset('path/to/coco8.zip')
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check_dataset("path/to/dataset.zip", task="detect")
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```
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Once your dataset ZIP is ready, navigate to the [Datasets](https://hub.ultralytics.com/datasets) page by clicking on the **Datasets** button in the sidebar.
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Once your dataset ZIP is ready, navigate to the [Datasets](https://hub.ultralytics.com/datasets) page by clicking on the **Datasets** button in the sidebar and click on the **Upload Dataset** button on the top right of the page.
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Click on the **Upload Dataset** button on the top right of the page. This action will trigger the **Upload Dataset** dialog.
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??? tip "Tip"
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You can upload a dataset directly from the [Home](https://hub.ultralytics.com/home) page.
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Upload your dataset in the _Dataset .zip file_ field.
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You have the additional option to set a custom name and description for your Ultralytics HUB dataset.
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This action will trigger the **Upload Dataset** dialog.
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Select the dataset task of your dataset and upload it in the _Dataset .zip file_ field.
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You have the additional option to set a custom name and description for your [Ultralytics HUB](https://bit.ly/ultralytics_hub) dataset.
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When you're happy with your dataset configuration, click **Upload**.
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After your dataset is uploaded and processed, you will be able to access it from the Datasets page.
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After your dataset is uploaded and processed, you will be able to access it from the [Datasets](https://hub.ultralytics.com/datasets) page.
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You can view the images in your dataset grouped by splits (Train, Validation, Test).
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!!! tip "Tip"
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??? tip "Tip"
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Each image can be enlarged for better visualization.
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Also, you can analyze your dataset by click on the **Overview** tab.
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Next, [train a model](./models.md#train-model) on your dataset.
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## Share Dataset
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!!! Info "Info"
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!!! info "Info"
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Ultralytics HUB's sharing functionality provides a convenient way to share datasets with others. This feature is designed to accommodate both existing Ultralytics HUB users and those who have yet to create an account.
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[Ultralytics HUB](https://bit.ly/ultralytics_hub)'s sharing functionality provides a convenient way to share datasets with others. This feature is designed to accommodate both existing [Ultralytics HUB](https://bit.ly/ultralytics_hub) users and those who have yet to create an account.
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!!! note "Note"
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You have control over the general access of your datasets.
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You can choose to set the general access to "Private", in which case, only you will have access to it. Alternatively, you can set the general access to "Unlisted" which grants viewing access to anyone who has the direct link to the dataset, regardless of whether they have an Ultralytics HUB account or not.
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You can choose to set the general access to "Private", in which case, only you will have access to it. Alternatively, you can set the general access to "Unlisted" which grants viewing access to anyone who has the direct link to the dataset, regardless of whether they have an [Ultralytics HUB](https://bit.ly/ultralytics_hub) account or not.
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Navigate to the Dataset page of the dataset you want to share, open the dataset actions dropdown and click on the **Share** option. This action will trigger the **Share Dataset** dialog.
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!!! tip "Tip"
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??? tip "Tip"
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You can also share a dataset directly from the [Datasets](https://hub.ultralytics.com/datasets) page.
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You can share a dataset directly from the [Datasets](https://hub.ultralytics.com/datasets) page.
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Set the general access to "Unlisted" and click **Save**.
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Now, anyone who has the direct link to your dataset can view it.
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||||
|
||||
!!! tip "Tip"
|
||||
??? tip "Tip"
|
||||
|
||||
You can easily click on the dataset's link shown in the **Share Dataset** dialog to copy it.
|
||||
|
||||
|
||||
|
||||
|
||||
## Edit / Delete Dataset
|
||||
## Edit Dataset
|
||||
|
||||
Navigate to the Dataset page of the dataset you want to edit, open the dataset actions dropdown and click on the **Edit** option. This action will trigger the **Update Dataset** dialog.
|
||||
|
||||
|
||||
|
||||
|
||||
??? tip "Tip"
|
||||
|
||||
You can edit a dataset directly from the [Datasets](https://hub.ultralytics.com/datasets) page.
|
||||
|
||||
|
||||
|
||||
Apply the desired modifications to your dataset and then confirm the changes by clicking **Save**.
|
||||
|
||||
|
||||
|
||||
## Delete Dataset
|
||||
|
||||
Navigate to the Dataset page of the dataset you want to delete, open the dataset actions dropdown and click on the **Delete** option. This action will delete the dataset.
|
||||
|
||||
|
||||
|
||||
??? tip "Tip"
|
||||
|
||||
You can delete a dataset directly from the [Datasets](https://hub.ultralytics.com/datasets) page.
|
||||
|
||||
|
||||
|
||||
!!! note "Note"
|
||||
|
||||
If you change your mind, you can restore the dataset from the [Trash](https://hub.ultralytics.com/trash) page.
|
||||
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -1,13 +1,14 @@
|
|||
---
|
||||
comments: true
|
||||
description: Gain insights into training and deploying your YOLOv5 and YOLOv8 models with Ultralytics HUB. Explore pre-trained models, templates and various integrations.
|
||||
keywords: Ultralytics HUB, YOLOv5, YOLOv8, model training, model deployment, pretrained models, model integrations
|
||||
description: Discover Ultralytics HUB for seamless training and deploying of YOLOv5 and YOLOv8 models. Start your AI journey with ease!.
|
||||
keywords: Ultralytics HUB, YOLO model training, YOLOv5, YOLOv8, model deployment, AI, machine learning
|
||||
---
|
||||
|
||||
# Ultralytics HUB
|
||||
|
||||
<a href="https://bit.ly/ultralytics_hub" target="_blank">
|
||||
<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png" alt="Ultralytics HUB preview image"></a>
|
||||
<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png" alt="Ultralytics HUB preview image">
|
||||
</a>
|
||||
<br>
|
||||
<div align="center">
|
||||
<a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="Ultralytics GitHub"></a>
|
||||
|
|
@ -26,36 +27,40 @@ keywords: Ultralytics HUB, YOLOv5, YOLOv8, model training, model deployment, pre
|
|||
<br>
|
||||
<br>
|
||||
<a href="https://github.com/ultralytics/hub/actions/workflows/ci.yaml">
|
||||
<img src="https://github.com/ultralytics/hub/actions/workflows/ci.yaml/badge.svg" alt="CI CPU"></a>
|
||||
<img src="https://github.com/ultralytics/hub/actions/workflows/ci.yaml/badge.svg" alt="CI"></a>
|
||||
<a href="https://colab.research.google.com/github/ultralytics/hub/blob/main/hub.ipynb">
|
||||
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
|
||||
</div>
|
||||
|
||||
👋 Hello from the [Ultralytics](https://ultralytics.com/) Team! We've been working hard these last few months to launch [Ultralytics HUB](https://bit.ly/ultralytics_hub), a new web tool for training and deploying all your YOLOv5 and YOLOv8 🚀 models from one spot!
|
||||
👋 Hello from the [Ultralytics](https://ultralytics.com/) Team!
|
||||
|
||||
We've been working hard over the past few months to launch [Ultralytics HUB](https://bit.ly/ultralytics_hub), a new platform for training, monitoring, and deploying all your YOLOv5 and YOLOv8 🚀 models from one spot!
|
||||
|
||||
## Introduction
|
||||
|
||||
HUB is designed to be user-friendly and intuitive, with a drag-and-drop interface that allows users to easily upload their data and train new models quickly. It offers a range of pre-trained models and templates to choose from, making it easy for users to get started with training their own models. Once a model is trained, it can be easily deployed and used for real-time object detection, instance segmentation and classification tasks.
|
||||
[Ultralytics HUB](https://bit.ly/ultralytics_hub) is designed to be user-friendly and intuitive, allowing users to quickly upload their datasets and train new YOLO models. It also offers a range of pre-trained models to choose from, making it extremely easy for users to get started. Once a model is trained, it can be effortlessly previewed in the [Ultralytics HUB App](app/index.md) before being deployed for real-time classification, object detection, and instance segmentation tasks.
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/lveF9iCMIzc?si=_Q4WB5kMB5qNe7q6"
|
||||
title="YouTube video player" frameborder="0"
|
||||
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
|
||||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> Train Your Custom YOLO Models In A Few Clicks with Ultralytics HUB.
|
||||
<strong>Watch:</strong> Train Your Custom YOLO Models In A Few Clicks with Ultralytics HUB
|
||||
</p>
|
||||
|
||||
We hope that the resources here will help you get the most out of HUB. Please browse the HUB <a href="https://docs.ultralytics.com/hub">Docs</a> for details, raise an issue on <a href="https://github.com/ultralytics/hub/issues/new/choose">GitHub</a> for support, and join our <a href="https://ultralytics.com/discord">Discord</a> community for questions and discussions!
|
||||
|
||||
- [**Quickstart**](quickstart.md). Start training and deploying YOLO models with HUB in seconds.
|
||||
- [**Datasets: Preparing and Uploading**](datasets.md). Learn how to prepare and upload your datasets to HUB in YOLO format.
|
||||
- [**Projects: Creating and Managing**](projects.md). Group your models into projects for improved organization.
|
||||
- [**Models: Training and Exporting**](models.md). Train YOLOv5 and YOLOv8 models on your custom datasets and export them to various formats for deployment.
|
||||
- [**Integrations: Options**](integrations.md). Explore different integration options for your trained models, such as TensorFlow, ONNX, OpenVINO, CoreML, and PaddlePaddle.
|
||||
- [**Ultralytics HUB App**](app/index.md). Learn about the Ultralytics App for iOS and Android, which allows you to run models directly on your mobile device.
|
||||
- [**iOS**](app/ios.md). Learn about YOLO CoreML models accelerated on Apple's Neural Engine on iPhones and iPads.
|
||||
- [**Android**](app/android.md). Explore TFLite acceleration on mobile devices.
|
||||
- [**Inference API**](inference-api.md). Understand how to use the Inference API for running your trained models in the cloud to generate predictions.
|
||||
- [**Quickstart**](quickstart.md): Start training and deploying models in seconds.
|
||||
- [**Datasets**](datasets.md): Learn how to prepare and upload your datasets.
|
||||
- [**Projects**](projects.md): Group your models into projects for improved organization.
|
||||
- [**Models**](models.md): Train models and export them to various formats for deployment.
|
||||
- [**Pro**](pro.md): Level up your experience by becoming a Pro user.
|
||||
- [**Cloud Training**](cloud-training.md): Understand how to train models using our Cloud Training solution.
|
||||
- [**Inference API**](inference-api.md): Understand how to use our Inference API.
|
||||
- [**Teams**](teams.md): Collaborate effortlessly with your team.
|
||||
- [**Integrations**](integrations.md): Explore different integration options.
|
||||
- [**Ultralytics HUB App**](app/index.md): Learn about the Ultralytics HUB App, which allows you to run models directly on your mobile device.
|
||||
- [**iOS**](app/ios.md): Explore CoreML acceleration on iPhones and iPads.
|
||||
- [**Android**](app/android.md): Explore TFLite acceleration on Android devices.
|
||||
|
|
@ -1,17 +1,16 @@
|
|||
---
|
||||
comments: true
|
||||
description: Access object detection capabilities of YOLOv8 via our RESTful API. Learn how to use the YOLO Inference API with Python or cURL for swift object detection.
|
||||
keywords: Ultralytics, YOLOv8, Inference API, object detection, RESTful API, Python, cURL, Quickstart
|
||||
description: Effortlessly run AI model inferences with Ultralytics HUB Inference API. Perfect for developers!.
|
||||
keywords: Ultralytics HUB, Inference API, YOLO, REST API, machine learning, AI model inference, remote inference
|
||||
---
|
||||
|
||||
# YOLO Inference API
|
||||
# Ultralytics HUB Inference API
|
||||
|
||||
The YOLO Inference API allows you to access the YOLOv8 object detection capabilities via a RESTful API. This enables you to run object detection on images without the need to install and set up the YOLOv8 environment locally.
|
||||
The [Ultralytics HUB](https://bit.ly/ultralytics_hub) Inference API allows you to run inference through our REST API without the need to install and set up the Ultralytics YOLO environment locally.
|
||||
|
||||
 Screenshot of the Inference API section in the trained model Preview tab.
|
||||
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/OpWpBI35A5Y"
|
||||
title="YouTube video player" frameborder="0"
|
||||
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
|
||||
|
|
@ -21,17 +20,9 @@ The YOLO Inference API allows you to access the YOLOv8 object detection capabili
|
|||
<strong>Watch:</strong> Ultralytics HUB Inference API Walkthrough
|
||||
</p>
|
||||
|
||||
## API URL
|
||||
## Python
|
||||
|
||||
The API URL is the address used to access the YOLO Inference API. In this case, the base URL is:
|
||||
|
||||
```
|
||||
https://api.ultralytics.com/v1/predict
|
||||
```
|
||||
|
||||
## Example Usage in Python
|
||||
|
||||
To access the YOLO Inference API with the specified model and API key using Python, you can use the following code:
|
||||
To access the [Ultralytics HUB](https://bit.ly/ultralytics_hub) Inference API using Python, use the following code:
|
||||
|
||||
```python
|
||||
import requests
|
||||
|
|
@ -53,11 +44,13 @@ with open("path/to/image.jpg", "rb") as image_file:
|
|||
print(response.json())
|
||||
```
|
||||
|
||||
In this example, replace `API_KEY` with your actual API key, `MODEL_ID` with the desired model ID, and `path/to/image.jpg` with the path to the image you want to analyze.
|
||||
!!! note "Note"
|
||||
|
||||
## Example Usage with cURL
|
||||
Replace `MODEL_ID` with the desired model ID, `API_KEY` with your actual API key, and `path/to/image.jpg` with the path to the image you want to run inference on.
|
||||
|
||||
You can use the YOLO Inference API with client URL (cURL) by utilizing the `curl` command. Replace `API_KEY` with your actual API key, `MODEL_ID` with the desired model ID, and `image.jpg` with the path to the image you want to analyze:
|
||||
## cURL
|
||||
|
||||
To access the [Ultralytics HUB](https://bit.ly/ultralytics_hub) Inference API using cURL, use the following code:
|
||||
|
||||
```bash
|
||||
curl -X POST "https://api.ultralytics.com/v1/predict/MODEL_ID" \
|
||||
|
|
@ -68,55 +61,97 @@ curl -X POST "https://api.ultralytics.com/v1/predict/MODEL_ID" \
|
|||
-F "iou=0.45"
|
||||
```
|
||||
|
||||
## Passing Arguments
|
||||
!!! note "Note"
|
||||
|
||||
This command sends a POST request to the YOLO Inference API with the specified `MODEL_ID` in the URL and the `API_KEY` in the request `headers`, along with the image file specified by `@path/to/image.jpg`.
|
||||
Replace `MODEL_ID` with the desired model ID, `API_KEY` with your actual API key, and `path/to/image.jpg` with the path to the image you want to run inference on.
|
||||
|
||||
Here's an example of passing the `size`, `confidence`, and `iou` arguments via the API URL using the `requests` library in Python:
|
||||
## Arguments
|
||||
|
||||
```python
|
||||
import requests
|
||||
See the table below for a full list of available inference arguments.
|
||||
|
||||
# API URL, use actual MODEL_ID
|
||||
url = f"https://api.ultralytics.com/v1/predict/MODEL_ID"
|
||||
| Argument | Default | Type | Description |
|
||||
| ------------ | ------- | ------- | -------------------------------------- |
|
||||
| `image` | | `image` | image file |
|
||||
| `url` | | `str` | URL of the image if not passing a file |
|
||||
| `size` | `640` | `int` | valid range `32` - `1280` pixels |
|
||||
| `confidence` | `0.25` | `float` | valid range `0.01` - `1.0` |
|
||||
| `iou` | `0.45` | `float` | valid range `0.0` - `0.95` |
|
||||
|
||||
# Headers, use actual API_KEY
|
||||
headers = {"x-api-key": "API_KEY"}
|
||||
## Response
|
||||
|
||||
# Inference arguments (optional)
|
||||
data = {"size": 640, "confidence": 0.25, "iou": 0.45}
|
||||
The [Ultralytics HUB](https://bit.ly/ultralytics_hub) Inference API returns a JSON response.
|
||||
|
||||
# Load image and send request
|
||||
with open("path/to/image.jpg", "rb") as image_file:
|
||||
files = {"image": image_file}
|
||||
response = requests.post(url, headers=headers, files=files, data=data)
|
||||
### Classification
|
||||
|
||||
print(response.json())
|
||||
```
|
||||
!!! Example "Classification Model"
|
||||
|
||||
In this example, the `data` dictionary contains the query arguments `size`, `confidence`, and `iou`, which tells the API to run inference at image size 640 with confidence and IoU thresholds of 0.25 and 0.45.
|
||||
=== "`ultralytics`"
|
||||
|
||||
This will send the query parameters along with the file in the POST request. See the table below for a full list of available inference arguments.
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
| Inference Argument | Default | Type | Notes |
|
||||
|--------------------|---------|---------|------------------------------------------------|
|
||||
| `size` | `640` | `int` | valid range is `32` - `1280` pixels |
|
||||
| `confidence` | `0.25` | `float` | valid range is `0.01` - `1.0` |
|
||||
| `iou` | `0.45` | `float` | valid range is `0.0` - `0.95` |
|
||||
| `url` | `''` | `str` | optional image URL if not image file is passed |
|
||||
| `normalize` | `False` | `bool` | |
|
||||
# Load model
|
||||
model = YOLO('yolov8n-cls.pt')
|
||||
|
||||
## Return JSON format
|
||||
# Run inference
|
||||
results = model('image.jpg')
|
||||
|
||||
The YOLO Inference API returns a JSON list with the detection results. The format of the JSON list will be the same as the one produced locally by the `results[0].tojson()` command.
|
||||
# Print image.jpg results in JSON format
|
||||
print(results[0].tojson())
|
||||
```
|
||||
|
||||
The JSON list contains information about the detected objects, their coordinates, classes, and confidence scores.
|
||||
=== "cURL"
|
||||
|
||||
### Detect Model Format
|
||||
```bash
|
||||
curl -X POST "https://api.ultralytics.com/v1/predict/MODEL_ID" \
|
||||
-H "x-api-key: API_KEY" \
|
||||
-F "image=@/path/to/image.jpg" \
|
||||
-F "size=640" \
|
||||
-F "confidence=0.25" \
|
||||
-F "iou=0.45"
|
||||
```
|
||||
|
||||
YOLO detection models, such as `yolov8n.pt`, can return JSON responses from local inference, cURL inference, and Python inference. All of these methods produce the same JSON response format.
|
||||
=== "Python"
|
||||
|
||||
!!! Example "Detect Model JSON Response"
|
||||
```python
|
||||
import requests
|
||||
|
||||
# API URL, use actual MODEL_ID
|
||||
url = f"https://api.ultralytics.com/v1/predict/MODEL_ID"
|
||||
|
||||
# Headers, use actual API_KEY
|
||||
headers = {"x-api-key": "API_KEY"}
|
||||
|
||||
# Inference arguments (optional)
|
||||
data = {"size": 640, "confidence": 0.25, "iou": 0.45}
|
||||
|
||||
# Load image and send request
|
||||
with open("path/to/image.jpg", "rb") as image_file:
|
||||
files = {"image": image_file}
|
||||
response = requests.post(url, headers=headers, files=files, data=data)
|
||||
|
||||
print(response.json())
|
||||
```
|
||||
|
||||
=== "Response"
|
||||
|
||||
```json
|
||||
{
|
||||
success: true,
|
||||
message: "Inference complete.",
|
||||
data: [
|
||||
{
|
||||
class: 0,
|
||||
name: "person",
|
||||
confidence: 0.92
|
||||
}
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
### Detection
|
||||
|
||||
!!! Example "Detection Model"
|
||||
|
||||
=== "`ultralytics`"
|
||||
|
||||
|
|
@ -166,55 +201,103 @@ YOLO detection models, such as `yolov8n.pt`, can return JSON responses from loca
|
|||
print(response.json())
|
||||
```
|
||||
|
||||
=== "JSON Response"
|
||||
=== "Response"
|
||||
|
||||
```json
|
||||
{
|
||||
"success": True,
|
||||
"message": "Inference complete.",
|
||||
"data": [
|
||||
success: true,
|
||||
message: "Inference complete.",
|
||||
data: [
|
||||
{
|
||||
"name": "person",
|
||||
"class": 0,
|
||||
"confidence": 0.8359682559967041,
|
||||
"box": {
|
||||
"x1": 0.08974208831787109,
|
||||
"y1": 0.27418340047200523,
|
||||
"x2": 0.8706787109375,
|
||||
"y2": 0.9887352837456598
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "person",
|
||||
"class": 0,
|
||||
"confidence": 0.8189555406570435,
|
||||
"box": {
|
||||
"x1": 0.5847355842590332,
|
||||
"y1": 0.05813225640190972,
|
||||
"x2": 0.8930277824401855,
|
||||
"y2": 0.9903111775716146
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "tie",
|
||||
"class": 27,
|
||||
"confidence": 0.2909725308418274,
|
||||
"box": {
|
||||
"x1": 0.3433395862579346,
|
||||
"y1": 0.6070465511745877,
|
||||
"x2": 0.40964522361755373,
|
||||
"y2": 0.9849439832899306
|
||||
}
|
||||
class: 0,
|
||||
name: "person",
|
||||
confidence: 0.92,
|
||||
width: 0.4893378019332886,
|
||||
height: 0.7437513470649719,
|
||||
xcenter: 0.4434437155723572,
|
||||
ycenter: 0.5198975801467896
|
||||
}
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
### Segment Model Format
|
||||
### OBB
|
||||
|
||||
YOLO segmentation models, such as `yolov8n-seg.pt`, can return JSON responses from local inference, cURL inference, and Python inference. All of these methods produce the same JSON response format.
|
||||
!!! Example "OBB Model"
|
||||
|
||||
!!! Example "Segment Model JSON Response"
|
||||
=== "`ultralytics`"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load model
|
||||
model = YOLO('yolov8n-obb.pt')
|
||||
|
||||
# Run inference
|
||||
results = model('image.jpg')
|
||||
|
||||
# Print image.jpg results in JSON format
|
||||
print(results[0].tojson())
|
||||
```
|
||||
|
||||
=== "cURL"
|
||||
|
||||
```bash
|
||||
curl -X POST "https://api.ultralytics.com/v1/predict/MODEL_ID" \
|
||||
-H "x-api-key: API_KEY" \
|
||||
-F "image=@/path/to/image.jpg" \
|
||||
-F "size=640" \
|
||||
-F "confidence=0.25" \
|
||||
-F "iou=0.45"
|
||||
```
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
import requests
|
||||
|
||||
# API URL, use actual MODEL_ID
|
||||
url = f"https://api.ultralytics.com/v1/predict/MODEL_ID"
|
||||
|
||||
# Headers, use actual API_KEY
|
||||
headers = {"x-api-key": "API_KEY"}
|
||||
|
||||
# Inference arguments (optional)
|
||||
data = {"size": 640, "confidence": 0.25, "iou": 0.45}
|
||||
|
||||
# Load image and send request
|
||||
with open("path/to/image.jpg", "rb") as image_file:
|
||||
files = {"image": image_file}
|
||||
response = requests.post(url, headers=headers, files=files, data=data)
|
||||
|
||||
print(response.json())
|
||||
```
|
||||
|
||||
=== "Response"
|
||||
|
||||
```json
|
||||
{
|
||||
success: true,
|
||||
message: "Inference complete.",
|
||||
data: [
|
||||
{
|
||||
class: 0,
|
||||
name: "person",
|
||||
confidence: 0.92,
|
||||
obb: [
|
||||
0.669310450553894,
|
||||
0.6247171759605408,
|
||||
0.9847468137741089,
|
||||
...
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
### Segmentation
|
||||
|
||||
!!! Example "Segmentation Model"
|
||||
|
||||
=== "`ultralytics`"
|
||||
|
||||
|
|
@ -264,98 +347,26 @@ YOLO segmentation models, such as `yolov8n-seg.pt`, can return JSON responses fr
|
|||
print(response.json())
|
||||
```
|
||||
|
||||
=== "JSON Response"
|
||||
=== "Response"
|
||||
|
||||
Note `segments` `x` and `y` lengths may vary from one object to another. Larger or more complex objects may have more segment points.
|
||||
```json
|
||||
{
|
||||
"success": True,
|
||||
"message": "Inference complete.",
|
||||
"data": [
|
||||
success: true,
|
||||
message: "Inference complete.",
|
||||
data: [
|
||||
{
|
||||
"name": "person",
|
||||
"class": 0,
|
||||
"confidence": 0.856913149356842,
|
||||
"box": {
|
||||
"x1": 0.1064866065979004,
|
||||
"y1": 0.2798851860894097,
|
||||
"x2": 0.8738358497619629,
|
||||
"y2": 0.9894873725043403
|
||||
},
|
||||
"segments": {
|
||||
"x": [
|
||||
0.421875,
|
||||
0.4203124940395355,
|
||||
0.41718751192092896
|
||||
...
|
||||
],
|
||||
"y": [
|
||||
0.2888889014720917,
|
||||
0.2916666567325592,
|
||||
0.2916666567325592
|
||||
...
|
||||
]
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "person",
|
||||
"class": 0,
|
||||
"confidence": 0.8512625694274902,
|
||||
"box": {
|
||||
"x1": 0.5757311820983887,
|
||||
"y1": 0.053943040635850696,
|
||||
"x2": 0.8960096359252929,
|
||||
"y2": 0.985154045952691
|
||||
},
|
||||
"segments": {
|
||||
"x": [
|
||||
0.7515624761581421,
|
||||
0.75,
|
||||
0.7437499761581421
|
||||
...
|
||||
],
|
||||
"y": [
|
||||
0.0555555559694767,
|
||||
0.05833333358168602,
|
||||
0.05833333358168602
|
||||
...
|
||||
]
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "tie",
|
||||
"class": 27,
|
||||
"confidence": 0.6485961675643921,
|
||||
"box": {
|
||||
"x1": 0.33911995887756347,
|
||||
"y1": 0.6057066175672743,
|
||||
"x2": 0.4081430912017822,
|
||||
"y2": 0.9916408962673611
|
||||
},
|
||||
"segments": {
|
||||
"x": [
|
||||
0.37187498807907104,
|
||||
0.37031251192092896,
|
||||
0.3687500059604645
|
||||
...
|
||||
],
|
||||
"y": [
|
||||
0.6111111044883728,
|
||||
0.6138888597488403,
|
||||
0.6138888597488403
|
||||
...
|
||||
]
|
||||
}
|
||||
class: 0,
|
||||
name: "person",
|
||||
confidence: 0.92,
|
||||
segment: [0.44140625, 0.15625, 0.439453125, ...]
|
||||
}
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
### Pose Model Format
|
||||
### Pose
|
||||
|
||||
YOLO pose models, such as `yolov8n-pose.pt`, can return JSON responses from local inference, cURL inference, and Python inference. All of these methods produce the same JSON response format.
|
||||
|
||||
!!! Example "Pose Model JSON Response"
|
||||
!!! Example "Pose Model"
|
||||
|
||||
=== "`ultralytics`"
|
||||
|
||||
|
|
@ -363,7 +374,7 @@ YOLO pose models, such as `yolov8n-pose.pt`, can return JSON responses from loca
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load model
|
||||
model = YOLO('yolov8n-seg.pt')
|
||||
model = YOLO('yolov8n-pose.pt')
|
||||
|
||||
# Run inference
|
||||
results = model('image.jpg')
|
||||
|
|
@ -405,75 +416,29 @@ YOLO pose models, such as `yolov8n-pose.pt`, can return JSON responses from loca
|
|||
print(response.json())
|
||||
```
|
||||
|
||||
=== "JSON Response"
|
||||
=== "Response"
|
||||
|
||||
Note COCO-keypoints pretrained models will have 17 human keypoints. The `visible` part of the keypoints indicates whether a keypoint is visible or obscured. Obscured keypoints may be outside the image or may not be visible, i.e. a person's eyes facing away from the camera.
|
||||
```json
|
||||
{
|
||||
"success": True,
|
||||
"message": "Inference complete.",
|
||||
"data": [
|
||||
success: true,
|
||||
message: "Inference complete.",
|
||||
data: [
|
||||
{
|
||||
"name": "person",
|
||||
"class": 0,
|
||||
"confidence": 0.8439509868621826,
|
||||
"box": {
|
||||
"x1": 0.1125,
|
||||
"y1": 0.28194444444444444,
|
||||
"x2": 0.7953125,
|
||||
"y2": 0.9902777777777778
|
||||
},
|
||||
"keypoints": {
|
||||
"x": [
|
||||
0.5058594942092896,
|
||||
0.5103894472122192,
|
||||
0.4920862317085266
|
||||
...
|
||||
],
|
||||
"y": [
|
||||
0.48964157700538635,
|
||||
0.4643048942089081,
|
||||
0.4465252459049225
|
||||
...
|
||||
],
|
||||
"visible": [
|
||||
0.8726999163627625,
|
||||
0.653947651386261,
|
||||
0.9130823612213135
|
||||
...
|
||||
]
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "person",
|
||||
"class": 0,
|
||||
"confidence": 0.7474289536476135,
|
||||
"box": {
|
||||
"x1": 0.58125,
|
||||
"y1": 0.0625,
|
||||
"x2": 0.8859375,
|
||||
"y2": 0.9888888888888889
|
||||
},
|
||||
"keypoints": {
|
||||
"x": [
|
||||
0.778544008731842,
|
||||
0.7976160049438477,
|
||||
0.7530890107154846
|
||||
...
|
||||
],
|
||||
"y": [
|
||||
0.27595141530036926,
|
||||
0.2378823608160019,
|
||||
0.23644638061523438
|
||||
...
|
||||
],
|
||||
"visible": [
|
||||
0.8900790810585022,
|
||||
0.789978563785553,
|
||||
0.8974530100822449
|
||||
...
|
||||
]
|
||||
}
|
||||
class: 0,
|
||||
name: "person",
|
||||
confidence: 0.92,
|
||||
keypoints: [
|
||||
0.5290805697441101,
|
||||
0.20698919892311096,
|
||||
1.0,
|
||||
0.5263055562973022,
|
||||
0.19584226608276367,
|
||||
1.0,
|
||||
0.5094948410987854,
|
||||
0.19120082259178162,
|
||||
1.0,
|
||||
...
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
|
|
|
|||
|
|
@ -1,64 +1,64 @@
|
|||
---
|
||||
comments: true
|
||||
description: Explore integration options for Ultralytics HUB. Currently featuring Roboflow for dataset integration and multiple export formats for your trained models.
|
||||
keywords: Ultralytics HUB, Integrations, Roboflow, Dataset, Export, YOLOv5, YOLOv8, ONNX, CoreML, TensorRT, TensorFlow
|
||||
description: Explore integration options for Ultralytics HUB. Currently featuring Roboflow for dataset integration and multiple export formats for your trained models. Discover what's next for Ultralytics with our under-construction page, previewing new, groundbreaking AI and ML features coming soon.
|
||||
keywords: Ultralytics HUB, Integrations, Roboflow, Dataset, Export, YOLOv5, YOLOv8, ONNX, CoreML, TensorRT, TensorFlow, coming soon, under construction, new features, AI updates, ML advancements, YOLO, technology preview
|
||||
---
|
||||
|
||||
# HUB Integrations
|
||||
# Ultralytics HUB Integrations - Under Construction 🏗️🌟
|
||||
|
||||
🚧 **Under Construction** 🚧
|
||||
Welcome to the Integrations guide for [Ultralytics HUB](https://bit.ly/ultralytics_hub)!
|
||||
|
||||
Welcome to the Integrations guide for [Ultralytics HUB](https://hub.ultralytics.com/)! We are in the process of expanding this section to provide you with comprehensive guidance on integrating your YOLOv5 and YOLOv8 models with various platforms and formats. Currently, Roboflow is our available dataset integration, with a wide range of export integrations for your trained models.
|
||||
We are in the process of expanding this section to provide you with comprehensive guidance on integrating your YOLOv5 and YOLOv8 models with various platforms and formats.
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/lveF9iCMIzc?si=_Q4WB5kMB5qNe7q6"
|
||||
title="YouTube video player" frameborder="0"
|
||||
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
|
||||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> Train Your Custom YOLO Models In A Few Clicks with Ultralytics HUB.
|
||||
</p>
|
||||
We appreciate your patience as we work to make this section comprehensive and user-friendly. Stay tuned for updates!
|
||||
|
||||
## Available Integrations
|
||||
### Available Integrations
|
||||
|
||||
### Dataset Integrations
|
||||
#### Dataset
|
||||
|
||||
- **Roboflow**: Seamlessly import your datasets for training.
|
||||
|
||||
### Export Integrations
|
||||
#### Export
|
||||
|
||||
Available export formats are in the table below. You can predict or validate directly on exported models using the `ultralytics` Python package, i.e. `yolo predict model=yolov8n.onnx`.
|
||||
|
||||
| Format | `format` Argument | Model | Metadata | Arguments |
|
||||
|---------------------------------------------------|-------------------|---------------------------|----------|----------------------------------------------------------------------|
|
||||
| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | - |
|
||||
| [TorchScript](../integrations/torchscript.md) | `torchscript` | `yolov8n.torchscript` | ✅ | `imgsz`, `optimize`, `batch` |
|
||||
| [ONNX](../integrations/onnx.md) | `onnx` | `yolov8n.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset`, `batch` |
|
||||
| [OpenVINO](../integrations/openvino.md) | `openvino` | `yolov8n_openvino_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` |
|
||||
| [TensorRT](../integrations/tensorrt.md) | `engine` | `yolov8n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `int8`, `batch` |
|
||||
| [CoreML](../integrations/coreml.md) | `coreml` | `yolov8n.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms`, `batch` |
|
||||
| [TF SavedModel](../integrations/tf-savedmodel.md) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `batch` |
|
||||
| [TF GraphDef](../integrations/tf-graphdef.md) | `pb` | `yolov8n.pb` | ❌ | `imgsz`, `batch` |
|
||||
| [TF Lite](../integrations/tflite.md) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` |
|
||||
| [TF Edge TPU](../integrations/edge-tpu.md) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz`, `batch` |
|
||||
| [TF.js](../integrations/tfjs.md) | `tfjs` | `yolov8n_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` |
|
||||
| [PaddlePaddle](../integrations/paddlepaddle.md) | `paddle` | `yolov8n_paddle_model/` | ✅ | `imgsz`, `batch` |
|
||||
| [NCNN](../integrations/ncnn.md) | `ncnn` | `yolov8n_ncnn_model/` | ✅ | `imgsz`, `half`, `batch` |
|
||||
| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | - |
|
||||
| [TorchScript](../integrations/torchscript.md) | `torchscript` | `yolov8n.torchscript` | ✅ | `imgsz`, `optimize`, `batch` |
|
||||
| [ONNX](../integrations/onnx.md) | `onnx` | `yolov8n.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset`, `batch` |
|
||||
| [OpenVINO](../integrations/openvino.md) | `openvino` | `yolov8n_openvino_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` |
|
||||
| [TensorRT](../integrations/tensorrt.md) | `engine` | `yolov8n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `int8`, `batch` |
|
||||
| [CoreML](../integrations/coreml.md) | `coreml` | `yolov8n.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms`, `batch` |
|
||||
| [TF SavedModel](../integrations/tf-savedmodel.md) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `batch` |
|
||||
| [TF GraphDef](../integrations/tf-graphdef.md) | `pb` | `yolov8n.pb` | ❌ | `imgsz`, `batch` |
|
||||
| [TF Lite](../integrations/tflite.md) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` |
|
||||
| [TF Edge TPU](../integrations/edge-tpu.md) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz`, `batch` |
|
||||
| [TF.js](../integrations/tfjs.md) | `tfjs` | `yolov8n_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` |
|
||||
| [PaddlePaddle](../integrations/paddlepaddle.md) | `paddle` | `yolov8n_paddle_model/` | ✅ | `imgsz`, `batch` |
|
||||
| [NCNN](../integrations/ncnn.md) | `ncnn` | `yolov8n_ncnn_model/` | ✅ | `imgsz`, `half`, `batch` |
|
||||
|
||||
## Coming Soon
|
||||
## Coming Soon 🎉
|
||||
|
||||
- Additional Dataset Integrations
|
||||
- Detailed Export Integration Guides
|
||||
- Step-by-Step Tutorials for Each Integration
|
||||
|
||||
## Need Immediate Assistance?
|
||||
## Stay Updated 🚧
|
||||
|
||||
While we're in the process of creating detailed guides:
|
||||
This placeholder page is your first stop for upcoming developments. Keep an eye out for:
|
||||
|
||||
- Browse through other [HUB Docs](./index.md) for detailed guides and tutorials.
|
||||
- Raise an issue on our [GitHub](https://github.com/ultralytics/hub/) for technical support.
|
||||
- Join our [Discord Community](https://ultralytics.com/discord/) for live discussions and community support.
|
||||
- **Newsletter:** Subscribe [here](https://ultralytics.com/#newsletter) for the latest news.
|
||||
- **Social Media:** Follow us [here](https://www.linkedin.com/company/ultralytics) for updates and teasers.
|
||||
- **Blog:** Visit our [blog](https://ultralytics.com/blog) for detailed insights.
|
||||
|
||||
We appreciate your patience as we work to make this section comprehensive and user-friendly. Stay tuned for updates!
|
||||
## We Value Your Input 🗣️
|
||||
|
||||
Your feedback shapes our future releases. Share your thoughts and suggestions [here](https://ultralytics.com/survey).
|
||||
|
||||
## Thank You, Community! 🌍
|
||||
|
||||
Your [contributions](https://docs.ultralytics.com/help/contributing) inspire our continuous [innovation](https://github.com/ultralytics/ultralytics). Stay tuned for the big reveal of what's next in AI and ML at Ultralytics!
|
||||
|
||||
---
|
||||
|
||||
Excited for what's coming? Bookmark this page and get ready for a transformative AI and ML journey with Ultralytics! 🛠️🤖
|
||||
|
|
|
|||
|
|
@ -1,23 +1,16 @@
|
|||
---
|
||||
comments: true
|
||||
description: Learn how to efficiently train AI models using Ultralytics HUB, a streamlined solution for model creation, training, evaluation, and deployment.
|
||||
keywords: Ultralytics, HUB Models, AI model training, model creation, model training, model evaluation, model deployment
|
||||
description: Streamline your AI model training on custom datasets with Ultralytics HUB. Efficient, user-friendly, and powerful.
|
||||
keywords: Ultralytics HUB, AI model training, custom datasets, YOLOv8, real-time updates, model deployment, Ultralytics
|
||||
---
|
||||
|
||||
# Ultralytics HUB Models
|
||||
|
||||
[Ultralytics HUB](https://hub.ultralytics.com/) models provide a streamlined solution for training vision AI models on custom datasets.
|
||||
[Ultralytics HUB](https://bit.ly/ultralytics_hub) models provide a streamlined solution for training vision AI models on custom datasets.
|
||||
|
||||
The process is user-friendly and efficient, involving a simple three-step creation and accelerated training powered by Ultralytics YOLOv8. Real-time updates on model metrics are available during training, allowing users to monitor progress at each step. Once training is completed, models can be previewed and easily deployed to real-world applications. Therefore, Ultralytics HUB offers a comprehensive yet straightforward system for model creation, training, evaluation, and deployment.
|
||||
|
||||
The entire process of training a model is detailed on our [Cloud Training Page](cloud-training.md).
|
||||
|
||||
|
||||
|
||||
## Train Model
|
||||
The process is user-friendly and efficient, involving a simple three-step creation and accelerated training powered by Ultralytics YOLOv8. During training, real-time updates on model metrics are available so that you can monitor each step of the progress. Once training is completed, you can preview your model and easily deploy it to real-world applications. Therefore, [Ultralytics HUB](https://bit.ly/ultralytics_hub) offers a comprehensive yet straightforward system for model creation, training, evaluation, and deployment.
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/YVlkq5H2tAQ"
|
||||
title="YouTube video player" frameborder="0"
|
||||
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
|
||||
|
|
@ -27,115 +20,268 @@ The entire process of training a model is detailed on our [Cloud Training Page](
|
|||
<strong>Watch:</strong> Ultralytics HUB Training and Validation Overview
|
||||
</p>
|
||||
|
||||
Navigate to the [Models](https://hub.ultralytics.com/models) page by clicking on the **Models** button in the sidebar.
|
||||
## Train Model
|
||||
|
||||
Training a model using HUB is a 4-step process:
|
||||
Navigate to the [Models](https://hub.ultralytics.com/models) page by clicking on the **Models** button in the sidebar and click on the **Train Model** button on the top right of the page.
|
||||
|
||||
- **Execute the pre-requisites script**: Run the provided scripts to prepare the virtual environment.
|
||||
- **Provide the API and start Training**: Once the model is prepared, provide the API key as instructed and execute the code block.
|
||||
- **Check the results and Metrics**: Upon successful execution, a link is provided to the Metrics Page. This page offers comprehensive details on the trained model, including specifications, loss metrics, dataset information, and image distributions. Additionally, the 'Deploy' tab provides access to the trained model's documentation and license details.
|
||||
- **Test your model**: Ultralytics HUB offers testing using custom images, device cameras, or links to test on `iPhone` or `Android` devices.
|
||||
|
||||
|
||||
|
||||
??? tip "Tip"
|
||||
|
||||
!!! tip "Tip"
|
||||
You can train a model directly from the [Home](https://hub.ultralytics.com/home) page.
|
||||
|
||||
You can also train a model directly from the [Home](https://hub.ultralytics.com/home) page.
|
||||
|
||||
|
||||
|
||||
|
||||
Click on the **Train Model** button on the top right of the page to trigger the **Train Model** dialog.
|
||||
|
||||
The **Train Model** dialog has three simple steps:
|
||||
This action will trigger the **Train Model** dialog which has three simple steps:
|
||||
|
||||
### 1. Dataset
|
||||
|
||||
Select the dataset for training and click **Continue**.
|
||||
In this step, you have to select the dataset you want to train your model on. After you selected a dataset, click **Continue**.
|
||||
|
||||
|
||||
|
||||
|
||||
??? tip "Tip"
|
||||
|
||||
You can skip this step if you train a model directly from the Dataset page.
|
||||
|
||||
|
||||
|
||||
### 2. Model
|
||||
|
||||
Choose the project, model name, and architecture. Read more about available architectures in our [YOLOv8](../models/yolov8.md) (and [YOLOv5](../models/yolov5.md)) documentation.
|
||||
In this step, you have to choose the project in which you want to create your model, the name of your model and your model's architecture.
|
||||
|
||||
Click **Continue** when satisfied with the configuration.
|
||||
|
||||
|
||||
|
||||
??? note "Note"
|
||||
|
||||
Ultralytics HUB will try to pre-select the project.
|
||||
|
||||
If you opened the **Train Model** dialog as described above, [Ultralytics HUB](https://bit.ly/ultralytics_hub) will pre-select the last project you used.
|
||||
|
||||
If you opened the **Train Model** dialog from the Project page, [Ultralytics HUB](https://bit.ly/ultralytics_hub) will pre-select the project you were inside of.
|
||||
|
||||
|
||||
|
||||
In case you don't have a project created yet, you can set the name of your project in this step and it will be created together with your model.
|
||||
|
||||
!!! Info "Info"
|
||||
|
||||
You can read more about the available [YOLOv8](https://docs.ultralytics.com/models/yolov8) (and [YOLOv5](https://docs.ultralytics.com/models/yolov5)) architectures in our documentation.
|
||||
|
||||
By default, your model will use a pre-trained model (trained on the [COCO](https://docs.ultralytics.com/datasets/detect/coco) dataset) to reduce training time. You can change this behavior and tweak your model's configuration by opening the **Advanced Model Configuration** accordion.
|
||||
|
||||
|
||||
|
||||
!!! note "Note"
|
||||
|
||||
By default, your model will use a pre-trained model (trained on the [COCO](../datasets/detect/coco.md) dataset) to reduce training time.
|
||||
You can easily change the most common model configuration options (such as the number of epochs) but you can also use the **Custom** option to access all [Train Settings](https://docs.ultralytics.com/modes/train/#train-settings) relevant to [Ultralytics HUB](https://bit.ly/ultralytics_hub).
|
||||
|
||||
Advanced options are available to modify this behavior.
|
||||
Alternatively, you start training from one of your previously trained models by clicking on the **Custom** tab.
|
||||
|
||||
## Preview Model
|
||||
|
||||
|
||||
Ultralytics HUB offers various ways to preview trained models.
|
||||
When you're happy with your model configuration, click **Continue**.
|
||||
|
||||
You can upload an image in the **Test** card under the **Preview** tab to preview your model.
|
||||
### 3. Train
|
||||
|
||||
|
||||
In this step, you will start training you model.
|
||||
|
||||
Use our Ultralytics Cloud API to effortlessly [run inference](inference-api.md) with your custom model.
|
||||
??? note "Note"
|
||||
|
||||
|
||||
When you are on this step, you have the option to close the **Train Model** dialog and start training your model from the Model page later.
|
||||
|
||||
Preview your model in real-time on your [iOS](https://apps.apple.com/xk/app/ultralytics/id1583935240) or [Android](https://play.google.com/store/apps/details?id=com.ultralytics.ultralytics_app) device by [downloading](https://ultralytics.com/app_install) our [Ultralytics HUB Mobile Application](app/index.md).
|
||||
|
||||
|
||||
|
||||
[Ultralytics HUB](https://bit.ly/ultralytics_hub) offers three training options:
|
||||
|
||||
## Train the model
|
||||
- [Ultralytics Cloud](./cloud-training.md)
|
||||
- Google Colab
|
||||
- Bring your own agent
|
||||
|
||||
Ultralytics HUB offers three training options:
|
||||
#### a. Ultralytics Cloud
|
||||
|
||||
- **Ultralytics Cloud** - Learn more about training via the Ultralytics [Cloud Training Page](cloud-training.md)
|
||||
- **Google Colab**
|
||||
- **Bring your own agent**
|
||||
You need to [upgrade](./pro.md#upgrade) to the [Pro Plan](./pro.md) in order to access [Ultralytics Cloud](./cloud-training.md).
|
||||
|
||||
## Training the Model on Google Colab
|
||||
|
||||
|
||||
To start training using Google Colab, follow the instructions on the Google Colab notebook.
|
||||
To train models using our [Cloud Training](./cloud-training.md) solution, read the [Ultralytics Cloud Training](./cloud-training.md) documentation.
|
||||
|
||||
<a href="https://colab.research.google.com/github/ultralytics/hub/blob/main/hub.ipynb" target="_blank">
|
||||
#### b. Google Colab
|
||||
|
||||
To start training your model using [Google Colab](https://colab.research.google.com/github/ultralytics/hub/blob/master/hub.ipynb), follow the instructions shown in the [Ultralytics HUB](https://bit.ly/ultralytics_hub) **Train Model** dialog or on the [Google Colab](https://colab.research.google.com/github/ultralytics/hub/blob/master/hub.ipynb) notebook.
|
||||
|
||||
<a href="https://colab.research.google.com/github/ultralytics/hub/blob/master/hub.ipynb" target="_blank">
|
||||
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab">
|
||||
</a>
|
||||
|
||||
|
||||
|
||||
|
||||
## Bring your own Agent
|
||||
When the training starts, you can click **Done** and monitor the training progress on the Model page.
|
||||
|
||||
Create an API endpoint through Ultralytics HUB to train the Model locally. Follow the provided steps, and access training details via the link generated on the Agent terminal.
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
!!! note "Note"
|
||||
|
||||
In case the training stops and a checkpoint was saved, you can resume training your model from the Model page.
|
||||
|
||||
|
||||
|
||||
#### c. Bring your own agent
|
||||
|
||||
To start training your model using your own agent, follow the instructions shown in the [Ultralytics HUB](https://bit.ly/ultralytics_hub) **Train Model** dialog.
|
||||
|
||||
|
||||
|
||||
Install the `ultralytics` package from [PyPI](https://pypi.org/project/ultralytics).
|
||||
|
||||
```bash
|
||||
pip install -U ultralytics
|
||||
```
|
||||
|
||||
Next, use the Python code provided to start training the model.
|
||||
|
||||
When the training starts, you can click **Done** and monitor the training progress on the Model page.
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
!!! note "Note"
|
||||
|
||||
In case the training stops and a checkpoint was saved, you can resume training your model from the Model page.
|
||||
|
||||
|
||||
|
||||
## Analyze Model
|
||||
|
||||
After you [train a model](#train-model), you can analyze the model metrics.
|
||||
|
||||
The **Train** tab presents the most important metrics carefully grouped based on the task.
|
||||
|
||||
|
||||
|
||||
To access all model metrics, click on the **Charts** tab.
|
||||
|
||||
|
||||
|
||||
??? tip "Tip"
|
||||
|
||||
Each chart can be enlarged for better visualization.
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Furthermore, to properly analyze the data, you can utilize the zoom feature.
|
||||
|
||||
|
||||
|
||||
## Preview Model
|
||||
|
||||
After you [train a model](#train-model), you can preview it by clicking on the **Preview** tab.
|
||||
|
||||
In the **Test** card, you can select a preview image from the dataset used during training or upload an image from your device.
|
||||
|
||||
|
||||
|
||||
!!! note "Note"
|
||||
|
||||
You can also use your camera to take a picture and run inference on it directly.
|
||||
|
||||
|
||||
|
||||
Furthermore, you can preview your model in real-time directly on your [iOS](https://apps.apple.com/xk/app/ultralytics/id1583935240) or [Android](https://play.google.com/store/apps/details?id=com.ultralytics.ultralytics_app) mobile device by [downloading](https://ultralytics.com/app_install) our [Ultralytics HUB App](app/index.md).
|
||||
|
||||
|
||||
|
||||
## Deploy Model
|
||||
|
||||
Export your model to 13 different formats, including ONNX, OpenVINO, CoreML, TensorFlow, Paddle, and more.
|
||||
After you [train a model](#train-model), you can export it to 13 different formats, including ONNX, OpenVINO, CoreML, TensorFlow, Paddle and many others.
|
||||
|
||||
|
||||
|
||||
|
||||
??? tip "Tip"
|
||||
|
||||
You can customize the export options of each format if you open the export actions dropdown and click on the **Advanced** option.
|
||||
|
||||
|
||||
|
||||
!!! note "Note"
|
||||
|
||||
You can re-export each format if you open the export actions dropdown and click on the **Advanced** option.
|
||||
|
||||
You can also use our [Inference API](./inference-api.md) in production.
|
||||
|
||||
|
||||
|
||||
Read the [Ultralytics Inference API](./inference-api.md) documentation for more information.
|
||||
|
||||
## Share Model
|
||||
|
||||
Ultralytics HUB's sharing functionality provides a convenient way to share models. Control the general access of your models, setting them to "Private" or "Unlisted".
|
||||
!!! info "Info"
|
||||
|
||||
Navigate to the Model page, open the model actions dropdown, and click on the **Share** option.
|
||||
[Ultralytics HUB](https://bit.ly/ultralytics_hub)'s sharing functionality provides a convenient way to share models with others. This feature is designed to accommodate both existing [Ultralytics HUB](https://bit.ly/ultralytics_hub) users and those who have yet to create an account.
|
||||
|
||||
|
||||
??? note "Note"
|
||||
|
||||
Set the general access and click **Save**.
|
||||
You have control over the general access of your models.
|
||||
|
||||
|
||||
You can choose to set the general access to "Private", in which case, only you will have access to it. Alternatively, you can set the general access to "Unlisted" which grants viewing access to anyone who has the direct link to the model, regardless of whether they have an [Ultralytics HUB](https://bit.ly/ultralytics_hub) account or not.
|
||||
|
||||
Now, anyone with the direct link can view your model.
|
||||
Navigate to the Model page of the model you want to share, open the model actions dropdown and click on the **Share** option. This action will trigger the **Share Model** dialog.
|
||||
|
||||
!!! tip "Tip"
|
||||
|
||||
|
||||
Easily copy the model's link shown in the **Share Model** dialog by clicking on it.
|
||||
??? tip "Tip"
|
||||
|
||||
You can also share a model directly from the [Models](https://hub.ultralytics.com/models) page or from the Project page of the project where your model is located.
|
||||
|
||||
|
||||
|
||||
Set the general access to "Unlisted" and click **Save**.
|
||||
|
||||
|
||||
|
||||
Now, anyone who has the direct link to your model can view it.
|
||||
|
||||
??? tip "Tip"
|
||||
|
||||
You can easily click on the model's link shown in the **Share Model** dialog to copy it.
|
||||
|
||||
|
||||
|
||||
## Edit and Delete Model
|
||||
## Edit Project
|
||||
|
||||
Navigate to the Model page, open the model actions dropdown, and click on the **Edit** option to update the model. To delete the model, select the **Delete** option.
|
||||
Navigate to the Model page of the model you want to edit, open the model actions dropdown and click on the **Edit** option. This action will trigger the **Update Model** dialog.
|
||||
|
||||
|
||||
|
||||
|
||||
??? tip "Tip"
|
||||
|
||||
You can also edit a model directly from the [Models](https://hub.ultralytics.com/models) page or from the Project page of the project where your model is located.
|
||||
|
||||
|
||||
|
||||
Apply the desired modifications to your model and then confirm the changes by clicking **Save**.
|
||||
|
||||
|
||||
|
||||
## Delete Project
|
||||
|
||||
Navigate to the Model page of the model you want to delete, open the model actions dropdown and click on the **Delete** option. This action will delete the model.
|
||||
|
||||
|
||||
|
||||
??? tip "Tip"
|
||||
|
||||
You can also delete a model directly from the [Models](https://hub.ultralytics.com/models) page or from the Project page of the project where your model is located.
|
||||
|
||||
|
||||
|
||||
!!! note "Note"
|
||||
|
||||
If you change your mind, you can restore the model from the [Trash](https://hub.ultralytics.com/trash) page.
|
||||
|
||||
|
||||
60
docs/en/hub/pro.md
Normal file
60
docs/en/hub/pro.md
Normal file
|
|
@ -0,0 +1,60 @@
|
|||
---
|
||||
comments: true
|
||||
description: Unlock premium features with Ultralytics HUB Pro! Get 200GB storage, cloud training, enhanced API limits, and more.
|
||||
keywords: Ultralytics HUB Pro, upgrade, premium features, cloud training, team collaboration, API limits
|
||||
---
|
||||
|
||||
# Ultralytics HUB Pro
|
||||
|
||||
[Ultralytics HUB](https://bit.ly/ultralytics_hub) offers the Pro Plan as a monthly or annual subscription.
|
||||
|
||||
The Pro Plan provides early access to upcoming features and includes enhanced benefits:
|
||||
|
||||
- 200GB of storage, compared to the standard 20GB.
|
||||
- Access to our [Cloud Training](./cloud-training.md).
|
||||
- Increased rate limits for our [Inference API](./inference-api.md).
|
||||
- Collaboration features for [teams](./teams.md).
|
||||
|
||||
## Upgrade
|
||||
|
||||
You can upgrade to the Pro Plan from the [Billing & License](https://hub.ultralytics.com/settings?tab=billing) tab on the [Settings](https://hub.ultralytics.com/settings) page by clicking on the **Upgrade** button.
|
||||
|
||||
|
||||
|
||||
Next, select the Pro Plan.
|
||||
|
||||
|
||||
|
||||
!!! tip "Tip"
|
||||
|
||||
You can save 20% if you choose the annual Pro Plan.
|
||||
|
||||
|
||||
|
||||
Fill in your details during the checkout!
|
||||
|
||||
|
||||
|
||||
!!! tip "Tip"
|
||||
|
||||
We recommend ticking the checkbox to save your payment information for future purchases, facilitating easier top-ups to your account balance.
|
||||
|
||||
That's it!
|
||||
|
||||
|
||||
|
||||
## Account Balance
|
||||
|
||||
The account balance is used to pay for [Ultralytics Cloud Training](./cloud-training.md) resources.
|
||||
|
||||
In order to top-up your account balance, simply click on the **Top-Up** button.
|
||||
|
||||
|
||||
|
||||
Next, set the amount you want to top-up.
|
||||
|
||||
|
||||
|
||||
That's it!
|
||||
|
||||
|
||||
|
|
@ -1,17 +1,16 @@
|
|||
---
|
||||
comments: true
|
||||
description: Learn how to manage Ultralytics HUB projects. Understand effective strategies to create, share, edit, delete, and compare models in an organized workspace.
|
||||
keywords: Ultralytics, HUB projects, Create project, Edit project, Share project, Delete project, Compare Models, Model Management
|
||||
description: Effortlessly consolidate, manage, and enhance your AI models with Ultralytics HUB projects. Start now!.
|
||||
keywords: Ultralytics HUB, manage AI models, project creation, model comparison, model management
|
||||
---
|
||||
|
||||
# Ultralytics HUB Projects
|
||||
|
||||
[Ultralytics HUB](https://hub.ultralytics.com/) projects provide an effective solution for consolidating and managing your models. If you are working with several models that perform similar tasks or have related purposes, Ultralytics HUB projects allow you to group these models together.
|
||||
[Ultralytics HUB](https://bit.ly/ultralytics_hub) projects provide an effective solution for consolidating and managing your models. If you are working with several models that perform similar tasks or have related purposes, [Ultralytics HUB](https://bit.ly/ultralytics_hub) projects allow you to group these models together.
|
||||
|
||||
This creates a unified and organized workspace that facilitates easier model management, comparison and development. Having similar models or various iterations together can facilitate rapid benchmarking, as you can compare their effectiveness. This can lead to faster, more insightful iterative development and refinement of your models.
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/Gc6K5eKrTNQ"
|
||||
title="YouTube video player" frameborder="0"
|
||||
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
|
||||
|
|
@ -23,47 +22,45 @@ This creates a unified and organized workspace that facilitates easier model man
|
|||
|
||||
## Create Project
|
||||
|
||||
Navigate to the [Projects](https://hub.ultralytics.com/projects) page by clicking on the **Projects** button in the sidebar.
|
||||
Navigate to the [Projects](https://hub.ultralytics.com/projects) page by clicking on the **Projects** button in the sidebar and click on the **Create Project** button on the top right of the page.
|
||||
|
||||
|
||||
|
||||
|
||||
??? tip "Tip"
|
||||
|
||||
You can also create a project directly from the [Home](https://hub.ultralytics.com/home) page.
|
||||
You can create a project directly from the [Home](https://hub.ultralytics.com/home) page.
|
||||
|
||||
|
||||
|
||||
|
||||
Click on the **Create Project** button on the top right of the page. This action will trigger the **Create Project** dialog, opening up a suite of options for tailoring your project to your needs.
|
||||
|
||||
|
||||
This action will trigger the **Create Project** dialog, opening up a suite of options for tailoring your project to your needs.
|
||||
|
||||
Type the name of your project in the _Project name_ field or keep the default name and finalize the project creation with a single click.
|
||||
|
||||
You have the additional option to enrich your project with a description and a unique image, enhancing its recognizability on the Projects page.
|
||||
You have the additional option to enrich your project with a description and a unique image, enhancing its recognizability on the [Projects](https://hub.ultralytics.com/projects) page.
|
||||
|
||||
When you're happy with your project configuration, click **Create**.
|
||||
|
||||
|
||||
|
||||
|
||||
After your project is created, you will be able to access it from the Projects page.
|
||||
After your project is created, you will be able to access it from the [Projects](https://hub.ultralytics.com/projects) page.
|
||||
|
||||
|
||||
|
||||
|
||||
Next, [train a model](./models.md#train-model) inside your project.
|
||||
|
||||
|
||||
|
||||
|
||||
## Share Project
|
||||
|
||||
!!! Info "Info"
|
||||
!!! info "Info"
|
||||
|
||||
Ultralytics HUB's sharing functionality provides a convenient way to share projects with others. This feature is designed to accommodate both existing Ultralytics HUB users and those who have yet to create an account.
|
||||
[Ultralytics HUB](https://bit.ly/ultralytics_hub)'s sharing functionality provides a convenient way to share projects with others. This feature is designed to accommodate both existing [Ultralytics HUB](https://bit.ly/ultralytics_hub) users and those who have yet to create an account.
|
||||
|
||||
??? note "Note"
|
||||
|
||||
You have control over the general access of your projects.
|
||||
|
||||
You can choose to set the general access to "Private", in which case, only you will have access to it. Alternatively, you can set the general access to "Unlisted" which grants viewing access to anyone who has the direct link to the project, regardless of whether they have an Ultralytics HUB account or not.
|
||||
You can choose to set the general access to "Private", in which case, only you will have access to it. Alternatively, you can set the general access to "Unlisted" which grants viewing access to anyone who has the direct link to the project, regardless of whether they have an [Ultralytics HUB](https://bit.ly/ultralytics_hub) account or not.
|
||||
|
||||
Navigate to the Project page of the project you want to share, open the project actions dropdown and click on the **Share** option. This action will trigger the **Share Project** dialog.
|
||||
|
||||
|
|
@ -71,7 +68,7 @@ Navigate to the Project page of the project you want to share, open the project
|
|||
|
||||
??? tip "Tip"
|
||||
|
||||
You can also share a project directly from the [Projects](https://hub.ultralytics.com/projects) page.
|
||||
You can share a project directly from the [Projects](https://hub.ultralytics.com/projects) page.
|
||||
|
||||
|
||||
|
||||
|
|
@ -99,7 +96,7 @@ Navigate to the Project page of the project you want to edit, open the project a
|
|||
|
||||
??? tip "Tip"
|
||||
|
||||
You can also edit a project directly from the [Projects](https://hub.ultralytics.com/projects) page.
|
||||
You can edit a project directly from the [Projects](https://hub.ultralytics.com/projects) page.
|
||||
|
||||
|
||||
|
||||
|
|
@ -115,7 +112,7 @@ Navigate to the Project page of the project you want to delete, open the project
|
|||
|
||||
??? tip "Tip"
|
||||
|
||||
You can also delete a project directly from the [Projects](https://hub.ultralytics.com/projects) page.
|
||||
You can delete a project directly from the [Projects](https://hub.ultralytics.com/projects) page.
|
||||
|
||||
|
||||
|
||||
|
|
@ -123,11 +120,11 @@ Navigate to the Project page of the project you want to delete, open the project
|
|||
|
||||
When deleting a project, the models inside the project will be deleted as well.
|
||||
|
||||
??? note "Note"
|
||||
!!! note "Note"
|
||||
|
||||
If you change your mind, you can restore the project from the [Trash](https://hub.ultralytics.com/trash) page.
|
||||
|
||||
|
||||
|
||||
|
||||
## Compare Models
|
||||
|
||||
|
|
@ -147,11 +144,15 @@ This will display all the relevant charts. Each chart corresponds to a different
|
|||
|
||||
|
||||
|
||||
Furthermore, to properly analyze the data, you can utilize the zoom feature.
|
||||
|
||||
|
||||
|
||||
??? tip "Tip"
|
||||
|
||||
You have the flexibility to customize your view by selectively hiding certain models. This feature allows you to concentrate on the models of interest.
|
||||
|
||||
|
||||
|
||||
|
||||
## Reorder Models
|
||||
|
||||
|
|
@ -177,4 +178,4 @@ Navigate to the Project page of the project where the model you want to mode is
|
|||
|
||||
Select the project you want to transfer the model to and click **Save**.
|
||||
|
||||
|
||||
|
||||
|
|
@ -1,58 +1,101 @@
|
|||
---
|
||||
comments: true
|
||||
description: Kickstart your journey with Ultralytics HUB. Learn how to train and deploy YOLOv5 and YOLOv8 models in seconds with our Quickstart guide.
|
||||
keywords: Ultralytics HUB, Quickstart, YOLOv5, YOLOv8, model training, quick deployment, drag-and-drop interface, real-time object detection
|
||||
description: Easily train YOLO models with the Ultralytics HUB. Quick sign-up, intuitive use, and ready-to-deploy models!.
|
||||
keywords: Ultralytics HUB, YOLO Quickstart, train YOLO model, pre-trained models, deploy models, Ultralytics HUB App
|
||||
---
|
||||
|
||||
# Quickstart Guide for Ultralytics HUB
|
||||
# Ultralytics HUB Quickstart
|
||||
|
||||
HUB is designed to be user-friendly and intuitive, with a drag-and-drop interface that allows users to easily upload their data and train new models quickly. It offers a range of pre-trained models and templates to choose from, making it easy for users to get started with training their own models. Once a model is trained, it can be easily deployed and used for real-time object detection, instance segmentation and classification tasks.
|
||||
[Ultralytics HUB](https://bit.ly/ultralytics_hub) is designed to be user-friendly and intuitive, allowing users to quickly upload their datasets and train new YOLO models. It also offers a range of pre-trained models to choose from, making it extremely easy for users to get started. Once a model is trained, it can be effortlessly previewed in the [Ultralytics HUB App](app/index.md) before being deployed for real-time classification, object detection, and instance segmentation tasks.
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/lveF9iCMIzc?si=_Q4WB5kMB5qNe7q6"
|
||||
title="YouTube video player" frameborder="0"
|
||||
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
|
||||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> Train Your Custom YOLO Models In A Few Clicks with Ultralytics HUB.
|
||||
<strong>Watch:</strong> Train Your Custom YOLO Models In A Few Clicks with Ultralytics HUB
|
||||
</p>
|
||||
|
||||
## Creating an Account
|
||||
## Get Started
|
||||
|
||||
[Ultralytics HUB](https://hub.ultralytics.com/) offers multiple easy account creation options. Users can register and sign in using Google, Apple, GitHub accounts, or a work email address.
|
||||
[Ultralytics HUB](https://bit.ly/ultralytics_hub) offers a variety easy of signup options. You can register and log in using your Google, Apple, or GitHub accounts, or simply with your email address.
|
||||
|
||||
|
||||
|
||||
|
||||
## The Dashboard
|
||||
During the signup, you will be asked to complete your profile.
|
||||
|
||||
Upon logging in, users are directed to the HUB dashboard, providing a comprehensive overview. The left pane conveniently offers links for tasks such as Uploading Datasets, Creating Projects, Training Models, Integrating Third-party Applications, Accessing Support, and Managing Trash.
|
||||
|
||||
|
||||
|
||||
??? tip "Tip"
|
||||
|
||||
## Selecting the Model
|
||||
You can update your profile from the [Account](https://hub.ultralytics.com/settings?tab=account) tab on the [Settings](https://hub.ultralytics.com/settings) page.
|
||||
|
||||
Choose a Dataset and train the model by selecting the Project name, Model name, and Architecture. Ultralytics offers a range of YOLOv8, YOLOv5, and YOLOv5u6 Architectures, including pre-trained and custom options.
|
||||
|
||||
|
||||
Read more about Models on the [HUB Models page](models.md).
|
||||
## Home
|
||||
|
||||
## Training the Model
|
||||
After signing in, you will be directed to the [Home](https://hub.ultralytics.com/home) page of [Ultralytics HUB](https://bit.ly/ultralytics_hub), which provides a comprehensive overview, quick links, and updates.
|
||||
|
||||
There are three ways to train your model: using Google Colab, training locally, or through Ultralytics Cloud. Learn more about training options on the [Cloud Training Page](cloud-training.md).
|
||||
The sidebar conveniently offers links to important modules of the platform, such as [Datasets](https://hub.ultralytics.com/datasets), [Projects](https://hub.ultralytics.com/projects), and [Models](https://hub.ultralytics.com/models).
|
||||
|
||||
## Integrating the Model
|
||||
|
||||
|
||||
Integrate your trained model with third-party applications or connect HUB from an external agent. Ultralytics HUB currently supports simple one-click API Integration with Roboflow. Read more about integration on the [Integration Page](integrations.md).
|
||||
### Recent
|
||||
|
||||
You can easily search globally or directly access your last updated [Datasets](https://hub.ultralytics.com/datasets), [Projects](https://hub.ultralytics.com/projects), or [Models](https://hub.ultralytics.com/models) using the Recent card on the [Home](https://hub.ultralytics.com/home) page.
|
||||
|
||||
|
||||
|
||||
### Upload Dataset
|
||||
|
||||
You can upload a dataset directly from the [Home](https://hub.ultralytics.com/home) page.
|
||||
|
||||
|
||||
|
||||
Read more about [datasets](https://docs.ultralytics.com/hub/datasets).
|
||||
|
||||
### Create Project
|
||||
|
||||
You can create a project directly from the [Home](https://hub.ultralytics.com/home) page.
|
||||
|
||||
|
||||
|
||||
Read more about [projects](https://docs.ultralytics.com/hub/projects).
|
||||
|
||||
### Train Model
|
||||
|
||||
You can train a model directly from the [Home](https://hub.ultralytics.com/home) page.
|
||||
|
||||
|
||||
|
||||
Read more about [models](https://docs.ultralytics.com/hub/models).
|
||||
|
||||
## Feedback
|
||||
|
||||
We value your feedback! Feel free to leave a review at any time.
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
??? info "Info"
|
||||
|
||||
Only our team will see your feedback, and we will use it to improve our platform.
|
||||
|
||||
## Need Help?
|
||||
|
||||
If you encounter any issues or have questions, we're here to assist you. You can report a bug, request a feature, or ask a question.
|
||||
If you encounter any issues or have questions, we're here to assist you.
|
||||
|
||||
|
||||
You can report a bug, request a feature, or ask a question on <a href="https://github.com/ultralytics/hub/issues/new/choose">GitHub</a>.
|
||||
|
||||
## Data Management
|
||||
!!! note "Note"
|
||||
|
||||
Manage your datasets efficiently with options to restore or permanently delete them from the Trash section in the left column.
|
||||
When reporting a bug, please include your Environment Details from the [Support](https://hub.ultralytics.com/support) page.
|
||||
|
||||
|
||||
|
||||
|
||||
??? tip "Tip"
|
||||
|
||||
You can join our <a href="https://ultralytics.com/discord">Discord</a> community for questions and discussions!
|
||||
|
|
@ -3,15 +3,17 @@ description: Discover what's next for Ultralytics with our under-construction pa
|
|||
keywords: Ultralytics, coming soon, under construction, new features, AI updates, ML advancements, YOLO, technology preview
|
||||
---
|
||||
|
||||
# Under Construction 🏗️🌟
|
||||
# Ultralytics HUB Teams - Under Construction 🏗️🌟
|
||||
|
||||
Welcome to the Ultralytics "Under Construction" page! Here, we're hard at work developing the next generation of AI and ML innovations. This page serves as a teaser for the exciting updates and new features we're eager to share with you!
|
||||
We are in the process of expanding this section to offer detailed guidance on how to effectively use the Teams features within [Ultralytics HUB](https://bit.ly/ultralytics_hub). This will include managing team members, sharing resources, and collaborating on projects.
|
||||
|
||||
We appreciate your patience as we work to make this section comprehensive and user-friendly. Stay tuned for updates!
|
||||
|
||||
## Exciting New Features on the Way 🎉
|
||||
|
||||
- **Innovative Breakthroughs:** Get ready for advanced features and services that will transform your AI and ML experience.
|
||||
- **New Horizons:** Anticipate novel products that redefine AI and ML capabilities.
|
||||
- **Enhanced Services:** We're upgrading our services for greater efficiency and user-friendliness.
|
||||
- Enhanced Team Management Features
|
||||
- Guidance on Resource Sharing Among Team Members
|
||||
- Best Practices for Collaborative Project Development
|
||||
|
||||
## Stay Updated 🚧
|
||||
|
||||
|
|
@ -23,11 +25,11 @@ This placeholder page is your first stop for upcoming developments. Keep an eye
|
|||
|
||||
## We Value Your Input 🗣️
|
||||
|
||||
Your feedback shapes our future releases. Share your thoughts and suggestions [here](https://ultralytics.com/contact).
|
||||
Your feedback shapes our future releases. Share your thoughts and suggestions [here](https://ultralytics.com/survey).
|
||||
|
||||
## Thank You, Community! 🌍
|
||||
|
||||
Your [contributions](../../help/contributing.md) inspire our continuous [innovation](https://github.com/ultralytics/ultralytics). Stay tuned for the big reveal of what's next in AI and ML at Ultralytics!
|
||||
Your [contributions](https://docs.ultralytics.com/help/contributing) inspire our continuous [innovation](https://github.com/ultralytics/ultralytics). Stay tuned for the big reveal of what's next in AI and ML at Ultralytics!
|
||||
|
||||
---
|
||||
|
||||
Loading…
Add table
Add a link
Reference in a new issue