Ultralytics HUB: Train Computer Vision Models on the Cloud 🚀
As the computer vision industry undergoes rapid transformation, we enter an era where the AI industry is soaring, much like a plane flying in the air. With the industry expanding at a remarkable pace, the demand from end-users is insatiable, particularly in the realm of deep learning algorithms such as Ultralytics YOLOv5, YOLOv8, and more.
Some clients seek to avoid the expenses associated with purchasing extensive GPU resources, while others prefer to save on dataset creation costs, opting to leverage pre-trained models for their specific use cases.
In this article, we’ll explore the newly launched cloud training feature in Ultralytics HUB:
- Ultralytics HUB Cloud Training Overview: Our no-code feature simplifies Ultralytics YOLO model training, enabling effortless use for non-coders and business owners. Just cover training costs, and we handle the rest, aligning with client goals.
- HUB Cloud Training Workflow: Ultralytics HUB cloud training simplifies the process with dataset uploading, model selection, and cloud instance setup. It involves an easy process to fine-tune pretrained models and export them for various applications.
Let’s dive in :)
Ultralytics HUB Cloud Training Overview
Typically, training deep learning models involves utilizing Python code or CLI commands. This process demands coding expertise to establish a suitable environment, and configuring GPU settings can be a challenge.
Our team acknowledges this challenge, and over the past 6 months, we’ve dedicated time to implementing a feature designed to offer a seamless no-code experience for end-users when they don’t want to utilize costly GPU systems for training the Ultralytics YOLO models and it supports various models, including YOLOv8 for object detection, object segmentation, pose estimation, and OBB models.
This means even a business owner with no coding experience can effortlessly leverage this feature. You only need to cover the training costs. This implementation aligns with the client’s goals, proving highly effective, as we handle everything else for you.
HUB Cloud Training Workflow
The Ultralytics HUB cloud training feature includes uploading datasets, creating projects, selecting models, and initializing cloud instances. Visual samples accompany each step to enhance comprehension.
Dataset Upload: This option allows users to upload their custom datasets for training purposes. For dataset format and detailed guidelines, you can refer to HUB dataset format
Model Selection: After uploading the dataset, users can choose the dataset and access the model selection page. This phase, the second and pivotal step in cloud training, empowers users to select pre-trained models for fine-tuning on their custom data.
Selection of Cloud Instance: Selecting the cloud instance marks the crucial final step, encompassing payment options and the chosen plan for utilization as shown in the mentioned figure below. It will then initialize the cloud training instance and can take ~5 minutes
Once you’ve chosen an instance, simply click the “Start Training” button to initialize the training process. Various training metrics will then be displayed, as shown in the figure below.
After the training, you can export or download the model for use in various applications such as Android or desktop applications.
Feel free to delve into the features of Ultralytics HUB. We can’t wait to see how you leverage these tools in your projects. Share your progress and discoveries with the community — dive in and showcase your work! 🚀
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Ultralytics is on a mission to empower people and companies to unleash the positive potential of vision AI. With a commitment to simplicity, flexibility, and usability, Ultralytics YOLO and Ultralytics HUB lower the barriers to getting started solving the world’s challenges. Ultralytics technology enables businesses to transform their operations, enhance customer experiences, and drive innovation by seamlessly integrating state-of-the-art models and user-friendly platforms. It’s been a remarkable journey, but we’re just getting started.
