README and Docs updates (#166)
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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Ayush Chaurasia
GitHub
@ -101,7 +101,7 @@ given task.
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| Key | Value | Description |
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|----------------|----------------------|-------------------------------------------------|
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| source | `ultralytics/assets` | Input source. Accepts image, folder, video, url |
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| view_img | `False` | View the prediction images |
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| show | `False` | View the prediction images |
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| save_txt | `False` | Save the results in a txt file |
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| save_conf | `False` | Save the condidence scores |
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| save_crop | `Fasle` | |
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@ -136,7 +136,7 @@ validation dataset and to detect and prevent overfitting.
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| dnn | `False` | Use OpenCV DNN for ONNX inference |
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| plots | `False` | |
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### Export settings
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### Export
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Export settings for YOLO models refer to the various configurations and options used to save or
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export the model for use in other environments or platforms. These settings can affect the model's performance, size,
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docs/hub.md
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docs/hub.md
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# Ultralytics HUB
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<div align="center">
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<a href="https://hub.ultralytics.com" target="_blank">
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<img width="1024" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png"></a>
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<br>
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<a href="https://github.com/ultralytics/hub/actions/workflows/ci.yaml">
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<img src="https://github.com/ultralytics/hub/actions/workflows/ci.yaml/badge.svg" alt="CI CPU"></a>
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</div>
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[Ultralytics HUB](https://hub.ultralytics.com) is a new no-code online tool developed
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by [Ultralytics](https://ultralytics.com), the creators of the popular [YOLOv5](https://github.com/ultralytics/yolov5)
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object detection and image segmentation models. With Ultralytics HUB, users can easily train and deploy YOLOv5 models
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without any coding or technical expertise.
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Ultralytics HUB is designed to be user-friendly and intuitive, with a drag-and-drop interface that allows users to
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easily upload their data and select their model configurations. It also offers a range of pre-trained models and
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templates to choose from, making it easy for users to get started with training their own models. Once a model is
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trained, it can be easily deployed and used for real-time object detection and image segmentation tasks. Overall,
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Ultralytics HUB is an essential tool for anyone looking to use YOLOv5 for their object detection and image segmentation
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projects.
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**[Get started now](https://hub.ultralytics.com)** and experience the power and simplicity of Ultralytics HUB for yourself. Sign up for a free account and
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start building, training, and deploying YOLOv5 and YOLOv8 models today.
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## 1. Upload a Dataset
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Ultralytics HUB datasets are just like YOLOv5 🚀 datasets, they use the same structure and the same label formats to keep everything simple.
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When you upload a dataset to Ultralytics HUB, make sure to **place your dataset YAML inside the dataset root directory** as in the example shown below, and then zip for upload to https://hub.ultralytics.com/. Your **dataset YAML, directory and zip** should all share the same name. For example, if your dataset is called 'coco6' as in our example [ultralytics/hub/coco6.zip](https://github.com/ultralytics/hub/blob/master/coco6.zip), then you should have a coco6.yaml inside your coco6/ directory, which should zip to create coco6.zip for upload:
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```bash
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zip -r coco6.zip coco6
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```
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The example [coco6.zip](https://github.com/ultralytics/hub/blob/master/coco6.zip) dataset in this repository can be downloaded and unzipped to see exactly how to structure your custom dataset.
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<p align="center"><img width="80%" src="https://user-images.githubusercontent.com/26833433/201424843-20fa081b-ad4b-4d6c-a095-e810775908d8.png" title="COCO6" /></p>
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The dataset YAML is the same standard YOLOv5 YAML format. See the [YOLOv5 Train Custom Data tutorial](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data) for full details.
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```yaml
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# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
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path: # dataset root dir (leave empty for HUB)
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train: images/train # train images (relative to 'path') 8 images
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val: images/val # val images (relative to 'path') 8 images
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test: # test images (optional)
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# Classes
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names:
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0: person
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1: bicycle
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2: car
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3: motorcycle
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...
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```
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After zipping your dataset, sign in to [Ultralytics HUB](https://bit.ly/ultralytics_hub) and click the Datasets tab. Click 'Upload Dataset' to upload, scan and visualize your new dataset before training new YOLOv5 models on it!
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<img width="100%" alt="HUB Dataset Upload" src="https://user-images.githubusercontent.com/26833433/198611715-540c9856-49d7-4069-a2fd-7c9eb70e772e.png">
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## 2. Train a Model
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Connect to the Ultralytics HUB notebook and use your model API key to begin training! <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>
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## 3. Deploy to Real World
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Export your model to 13 different formats, including TensorFlow, ONNX, OpenVINO, CoreML, Paddle and many others. Run models directly on your mobile device by downloading the [Ultralytics App](https://ultralytics.com/app_install)!
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<a align="center" href="https://ultralytics.com/app_install" target="_blank">
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<img width="100%" alt="Ultralytics mobile app" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-app.png"></a>
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## ❓ Issues
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If you are a new [Ultralytics HUB](https://bit.ly/ultralytics_hub) user and have questions or comments, you are in the right place! Please raise a [New Issue](https://github.com/ultralytics/hub/issues/new/choose) and let us know what we can do to make your life better 😃!
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@ -35,7 +35,7 @@ This is the simplest way of simply using yolo models in a python environment. It
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model = YOLO("model.pt")
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model.predict(source="0") # accepts all formats - img/folder/vid.*(mp4/format). 0 for webcam
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model.predict(source="folder", view_img=True) # Display preds. Accepts all yolo predict arguments
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model.predict(source="folder", show=True) # Display preds. Accepts all yolo predict arguments
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```
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