diff --git a/docs/README.md b/docs/README.md index 565a0010..b3766abe 100644 --- a/docs/README.md +++ b/docs/README.md @@ -107,7 +107,7 @@ Choose a hosting provider and deployment method for your MkDocs documentation: - Update the "Custom domain" in your repository's settings for a personalized URL. -![196814117-fc16e711-d2be-4722-9536-b7c6d78fd167](https://user-images.githubusercontent.com/26833433/210150206-9e86dcd7-10af-43e4-9eb2-9518b3799eac.png) +![MkDocs deployment example](https://github.com/ultralytics/docs/releases/download/0/mkdocs-deployment-example.avif) - For detailed deployment guidance, consult the [MkDocs documentation](https://www.mkdocs.org/user-guide/deploying-your-docs/). @@ -115,7 +115,7 @@ Choose a hosting provider and deployment method for your MkDocs documentation: We cherish the community's input as it drives Ultralytics open-source initiatives. Dive into the [Contributing Guide](https://docs.ultralytics.com/help/contributing) and share your thoughts via our [Survey](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey). A heartfelt thank you 🙏 to each contributor! -![Ultralytics open-source contributors](https://github.com/ultralytics/assets/raw/main/im/image-contributors.png) +![Ultralytics open-source contributors](https://github.com/ultralytics/docs/releases/download/0/ultralytics-open-source-contributors.avif) ## 📜 License diff --git a/docs/en/datasets/classify/caltech101.md b/docs/en/datasets/classify/caltech101.md index 6a75f66a..7029c5e6 100644 --- a/docs/en/datasets/classify/caltech101.md +++ b/docs/en/datasets/classify/caltech101.md @@ -53,7 +53,7 @@ To train a YOLO model on the Caltech-101 dataset for 100 epochs, you can use the The Caltech-101 dataset contains high-quality color images of various objects, providing a well-structured dataset for object recognition tasks. Here are some examples of images from the dataset: -![Dataset sample image](https://user-images.githubusercontent.com/26833433/239366386-44171121-b745-4206-9b59-a3be41e16089.png) +![Dataset sample image](https://github.com/ultralytics/docs/releases/download/0/caltech101-sample-image.avif) The example showcases the variety and complexity of the objects in the Caltech-101 dataset, emphasizing the significance of a diverse dataset for training robust object recognition models. diff --git a/docs/en/datasets/classify/caltech256.md b/docs/en/datasets/classify/caltech256.md index c7b367cc..a2551b9a 100644 --- a/docs/en/datasets/classify/caltech256.md +++ b/docs/en/datasets/classify/caltech256.md @@ -64,7 +64,7 @@ To train a YOLO model on the Caltech-256 dataset for 100 epochs, you can use the The Caltech-256 dataset contains high-quality color images of various objects, providing a comprehensive dataset for object recognition tasks. Here are some examples of images from the dataset ([credit](https://ml4a.github.io/demos/tsne_viewer.html)): -![Dataset sample image](https://user-images.githubusercontent.com/26833433/239365061-1e5f7857-b1e8-44ca-b3d7-d0befbcd33f9.jpg) +![Dataset sample image](https://github.com/ultralytics/docs/releases/download/0/caltech256-sample-image.avif) The example showcases the diversity and complexity of the objects in the Caltech-256 dataset, emphasizing the importance of a varied dataset for training robust object recognition models. diff --git a/docs/en/datasets/classify/cifar10.md b/docs/en/datasets/classify/cifar10.md index 54f9e9c2..39762681 100644 --- a/docs/en/datasets/classify/cifar10.md +++ b/docs/en/datasets/classify/cifar10.md @@ -67,7 +67,7 @@ To train a YOLO model on the CIFAR-10 dataset for 100 epochs with an image size The CIFAR-10 dataset contains color images of various objects, providing a well-structured dataset for image classification tasks. Here are some examples of images from the dataset: -![Dataset sample image](https://miro.medium.com/max/1100/1*SZnidBt7CQ4Xqcag6rd8Ew.png) +![Dataset sample image](https://github.com/ultralytics/docs/releases/download/0/cifar10-sample-image.avif) The example showcases the variety and complexity of the objects in the CIFAR-10 dataset, highlighting the importance of a diverse dataset for training robust image classification models. diff --git a/docs/en/datasets/classify/cifar100.md b/docs/en/datasets/classify/cifar100.md index 4a8ba4bd..722eccf9 100644 --- a/docs/en/datasets/classify/cifar100.md +++ b/docs/en/datasets/classify/cifar100.md @@ -56,7 +56,7 @@ To train a YOLO model on the CIFAR-100 dataset for 100 epochs with an image size The CIFAR-100 dataset contains color images of various objects, providing a well-structured dataset for image classification tasks. Here are some examples of images from the dataset: -![Dataset sample image](https://user-images.githubusercontent.com/26833433/239363319-62ebf02f-7469-4178-b066-ccac3cd334db.jpg) +![Dataset sample image](https://github.com/ultralytics/docs/releases/download/0/cifar100-sample-image.avif) The example showcases the variety and complexity of the objects in the CIFAR-100 dataset, highlighting the importance of a diverse dataset for training robust image classification models. diff --git a/docs/en/datasets/classify/fashion-mnist.md b/docs/en/datasets/classify/fashion-mnist.md index 656473ed..674e0858 100644 --- a/docs/en/datasets/classify/fashion-mnist.md +++ b/docs/en/datasets/classify/fashion-mnist.md @@ -81,7 +81,7 @@ To train a CNN model on the Fashion-MNIST dataset for 100 epochs with an image s The Fashion-MNIST dataset contains grayscale images of Zalando's article images, providing a well-structured dataset for image classification tasks. Here are some examples of images from the dataset: -![Dataset sample image](https://user-images.githubusercontent.com/26833433/239359139-ce0a434e-9056-43e0-a306-3214f193dcce.png) +![Dataset sample image](https://github.com/ultralytics/docs/releases/download/0/fashion-mnist-sample.avif) The example showcases the variety and complexity of the images in the Fashion-MNIST dataset, highlighting the importance of a diverse dataset for training robust image classification models. diff --git a/docs/en/datasets/classify/imagenet.md b/docs/en/datasets/classify/imagenet.md index 53aabcce..6ec3f920 100644 --- a/docs/en/datasets/classify/imagenet.md +++ b/docs/en/datasets/classify/imagenet.md @@ -66,7 +66,7 @@ To train a deep learning model on the ImageNet dataset for 100 epochs with an im The ImageNet dataset contains high-resolution images spanning thousands of object categories, providing a diverse and extensive dataset for training and evaluating computer vision models. Here are some examples of images from the dataset: -![Dataset sample images](https://user-images.githubusercontent.com/26833433/239280348-3d8f30c7-6f05-4dda-9cfe-d62ad9faecc9.png) +![Dataset sample images](https://github.com/ultralytics/docs/releases/download/0/imagenet-sample-images.avif) The example showcases the variety and complexity of the images in the ImageNet dataset, highlighting the importance of a diverse dataset for training robust computer vision models. diff --git a/docs/en/datasets/classify/imagenet10.md b/docs/en/datasets/classify/imagenet10.md index a079986c..cc9c9ec7 100644 --- a/docs/en/datasets/classify/imagenet10.md +++ b/docs/en/datasets/classify/imagenet10.md @@ -52,7 +52,7 @@ To test a deep learning model on the ImageNet10 dataset with an image size of 22 The ImageNet10 dataset contains a subset of images from the original ImageNet dataset. These images are chosen to represent the first 10 classes in the dataset, providing a diverse yet compact dataset for quick testing and evaluation. -![Dataset sample images](https://user-images.githubusercontent.com/26833433/239689723-16f9b4a7-becc-4deb-b875-d3e5c28eb03b.png) The example showcases the variety and complexity of the images in the ImageNet10 dataset, highlighting its usefulness for sanity checks and quick testing of computer vision models. +![Dataset sample images](https://github.com/ultralytics/docs/releases/download/0/imagenet10-sample-images.avif) The example showcases the variety and complexity of the images in the ImageNet10 dataset, highlighting its usefulness for sanity checks and quick testing of computer vision models. ## Citations and Acknowledgments diff --git a/docs/en/datasets/classify/imagenette.md b/docs/en/datasets/classify/imagenette.md index 9a2a128f..aea183f3 100644 --- a/docs/en/datasets/classify/imagenette.md +++ b/docs/en/datasets/classify/imagenette.md @@ -54,7 +54,7 @@ To train a model on the ImageNette dataset for 100 epochs with a standard image The ImageNette dataset contains colored images of various objects and scenes, providing a diverse dataset for image classification tasks. Here are some examples of images from the dataset: -![Dataset sample image](https://docs.fast.ai/22_tutorial.imagenette_files/figure-html/cell-21-output-1.png) +![Dataset sample image](https://github.com/ultralytics/docs/releases/download/0/imagenette-sample-image.avif) The example showcases the variety and complexity of the images in the ImageNette dataset, highlighting the importance of a diverse dataset for training robust image classification models. diff --git a/docs/en/datasets/classify/imagewoof.md b/docs/en/datasets/classify/imagewoof.md index e6668dfc..0d768b07 100644 --- a/docs/en/datasets/classify/imagewoof.md +++ b/docs/en/datasets/classify/imagewoof.md @@ -89,7 +89,7 @@ It's important to note that using smaller images will likely yield lower perform The ImageWoof dataset contains colorful images of various dog breeds, providing a challenging dataset for image classification tasks. Here are some examples of images from the dataset: -![Dataset sample image](https://user-images.githubusercontent.com/26833433/239357533-ec833254-4351-491b-8cb3-59578ea5d0b2.png) +![Dataset sample image](https://github.com/ultralytics/docs/releases/download/0/imagewoof-dataset-sample.avif) The example showcases the subtle differences and similarities among the different dog breeds in the ImageWoof dataset, highlighting the complexity and difficulty of the classification task. diff --git a/docs/en/datasets/detect/african-wildlife.md b/docs/en/datasets/detect/african-wildlife.md index 2c5b346a..bdd392cd 100644 --- a/docs/en/datasets/detect/african-wildlife.md +++ b/docs/en/datasets/detect/african-wildlife.md @@ -91,7 +91,7 @@ To train a YOLOv8n model on the African wildlife dataset for 100 epochs with an The African wildlife dataset comprises a wide variety of images showcasing diverse animal species and their natural habitats. Below are examples of images from the dataset, each accompanied by its corresponding annotations. -![African wildlife dataset sample image](https://github.com/RizwanMunawar/RizwanMunawar/assets/62513924/919f8190-ccf3-4a96-a5f1-55d9eebc77ec) +![African wildlife dataset sample image](https://github.com/ultralytics/docs/releases/download/0/african-wildlife-dataset-sample.avif) - **Mosaiced Image**: Here, we present a training batch consisting of mosaiced dataset images. Mosaicing, a training technique, combines multiple images into one, enriching batch diversity. This method helps enhance the model's ability to generalize across different object sizes, aspect ratios, and contexts. diff --git a/docs/en/datasets/detect/argoverse.md b/docs/en/datasets/detect/argoverse.md index 985ceca1..56023b6b 100644 --- a/docs/en/datasets/detect/argoverse.md +++ b/docs/en/datasets/detect/argoverse.md @@ -70,7 +70,7 @@ To train a YOLOv8n model on the Argoverse dataset for 100 epochs with an image s The Argoverse dataset contains a diverse set of sensor data, including camera images, LiDAR point clouds, and HD map information, providing rich context for autonomous driving tasks. Here are some examples of data from the dataset, along with their corresponding annotations: -![Dataset sample image](https://www.argoverse.org/assets/images/reference_images/av2_ground_height.png) +![Dataset sample image](https://github.com/ultralytics/docs/releases/download/0/argoverse-3d-tracking-sample.avif) - **Argoverse 3D Tracking**: This image demonstrates an example of 3D object tracking, where objects are annotated with 3D bounding boxes. The dataset provides LiDAR point clouds and camera images to facilitate the development of models for this task. diff --git a/docs/en/datasets/detect/brain-tumor.md b/docs/en/datasets/detect/brain-tumor.md index a36fab40..4ec217d5 100644 --- a/docs/en/datasets/detect/brain-tumor.md +++ b/docs/en/datasets/detect/brain-tumor.md @@ -90,7 +90,7 @@ To train a YOLOv8n model on the brain tumor dataset for 100 epochs with an image The brain tumor dataset encompasses a wide array of images featuring diverse object categories and intricate scenes. Presented below are examples of images from the dataset, accompanied by their respective annotations -![Brain tumor dataset sample image](https://github.com/RizwanMunawar/RizwanMunawar/assets/62513924/1741cbf5-2462-4e9a-b0b9-4a07d76cf7ef) +![Brain tumor dataset sample image](https://github.com/ultralytics/docs/releases/download/0/brain-tumor-dataset-sample-image.avif) - **Mosaiced Image**: Displayed here is a training batch comprising mosaiced dataset images. Mosaicing, a training technique, consolidates multiple images into one, enhancing batch diversity. This approach aids in improving the model's capacity to generalize across various object sizes, aspect ratios, and contexts. diff --git a/docs/en/datasets/detect/coco.md b/docs/en/datasets/detect/coco.md index 733c3a1d..d3b0589e 100644 --- a/docs/en/datasets/detect/coco.md +++ b/docs/en/datasets/detect/coco.md @@ -87,7 +87,7 @@ To train a YOLOv8n model on the COCO dataset for 100 epochs with an image size o The COCO dataset contains a diverse set of images with various object categories and complex scenes. Here are some examples of images from the dataset, along with their corresponding annotations: -![Dataset sample image](https://user-images.githubusercontent.com/26833433/236811818-5b566576-1e92-42fa-9462-4b6a848abe89.jpg) +![Dataset sample image](https://github.com/ultralytics/docs/releases/download/0/mosaiced-coco-dataset-sample.avif) - **Mosaiced Image**: This image demonstrates a training batch composed of mosaiced dataset images. Mosaicing is a technique used during training that combines multiple images into a single image to increase the variety of objects and scenes within each training batch. This helps improve the model's ability to generalize to different object sizes, aspect ratios, and contexts. diff --git a/docs/en/datasets/detect/coco8.md b/docs/en/datasets/detect/coco8.md index 6577ab0f..cae9e673 100644 --- a/docs/en/datasets/detect/coco8.md +++ b/docs/en/datasets/detect/coco8.md @@ -62,7 +62,7 @@ To train a YOLOv8n model on the COCO8 dataset for 100 epochs with an image size Here are some examples of images from the COCO8 dataset, along with their corresponding annotations: -Dataset sample image +Dataset sample image - **Mosaiced Image**: This image demonstrates a training batch composed of mosaiced dataset images. Mosaicing is a technique used during training that combines multiple images into a single image to increase the variety of objects and scenes within each training batch. This helps improve the model's ability to generalize to different object sizes, aspect ratios, and contexts. diff --git a/docs/en/datasets/detect/globalwheat2020.md b/docs/en/datasets/detect/globalwheat2020.md index 28c95c10..a8e255b5 100644 --- a/docs/en/datasets/detect/globalwheat2020.md +++ b/docs/en/datasets/detect/globalwheat2020.md @@ -65,7 +65,7 @@ To train a YOLOv8n model on the Global Wheat Head Dataset for 100 epochs with an The Global Wheat Head Dataset contains a diverse set of outdoor field images, capturing the natural variability in wheat head appearances, environments, and conditions. Here are some examples of data from the dataset, along with their corresponding annotations: -![Dataset sample image](https://i.ytimg.com/vi/yqvMuw-uedU/maxresdefault.jpg) +![Dataset sample image](https://github.com/ultralytics/docs/releases/download/0/wheat-head-detection-sample.avif) - **Wheat Head Detection**: This image demonstrates an example of wheat head detection, where wheat heads are annotated with bounding boxes. The dataset provides a variety of images to facilitate the development of models for this task. diff --git a/docs/en/datasets/detect/index.md b/docs/en/datasets/detect/index.md index 97806cb0..934fe385 100644 --- a/docs/en/datasets/detect/index.md +++ b/docs/en/datasets/detect/index.md @@ -34,15 +34,15 @@ names: Labels for this format should be exported to YOLO format with one `*.txt` file per image. If there are no objects in an image, no `*.txt` file is required. The `*.txt` file should be formatted with one row per object in `class x_center y_center width height` format. Box coordinates must be in **normalized xywh** format (from 0 to 1). If your boxes are in pixels, you should divide `x_center` and `width` by image width, and `y_center` and `height` by image height. Class numbers should be zero-indexed (start with 0). -

Example labelled image

+

Example labelled image

The label file corresponding to the above image contains 2 persons (class `0`) and a tie (class `27`): -

Example label file

+

Example label file

When using the Ultralytics YOLO format, organize your training and validation images and labels as shown in the [COCO8 dataset](coco8.md) example below. -

Example dataset directory structure

+

Example dataset directory structure

## Usage diff --git a/docs/en/datasets/detect/lvis.md b/docs/en/datasets/detect/lvis.md index 3c52541c..2cf6a0c2 100644 --- a/docs/en/datasets/detect/lvis.md +++ b/docs/en/datasets/detect/lvis.md @@ -20,7 +20,7 @@ The [LVIS dataset](https://www.lvisdataset.org/) is a large-scale, fine-grained

- LVIS Dataset example images + LVIS Dataset example images

## Key Features @@ -83,7 +83,7 @@ To train a YOLOv8n model on the LVIS dataset for 100 epochs with an image size o The LVIS dataset contains a diverse set of images with various object categories and complex scenes. Here are some examples of images from the dataset, along with their corresponding annotations: -![LVIS Dataset sample image](https://github.com/ultralytics/ultralytics/assets/26833433/38cc033a-68b0-47f3-a5b8-4ef554362e40) +![LVIS Dataset sample image](https://github.com/ultralytics/docs/releases/download/0/lvis-mosaiced-training-batch.avif) - **Mosaiced Image**: This image demonstrates a training batch composed of mosaiced dataset images. Mosaicing is a technique used during training that combines multiple images into a single image to increase the variety of objects and scenes within each training batch. This helps improve the model's ability to generalize to different object sizes, aspect ratios, and contexts. @@ -154,6 +154,6 @@ Ultralytics YOLO models, including the latest YOLOv8, are optimized for real-tim Yes, the LVIS dataset includes a variety of images with diverse object categories and complex scenes. Here is an example of a sample image along with its annotations: -![LVIS Dataset sample image](https://github.com/ultralytics/ultralytics/assets/26833433/38cc033a-68b0-47f3-a5b8-4ef554362e40) +![LVIS Dataset sample image](https://github.com/ultralytics/docs/releases/download/0/lvis-mosaiced-training-batch.avif) This mosaiced image demonstrates a training batch composed of multiple dataset images combined into one. Mosaicing increases the variety of objects and scenes within each training batch, enhancing the model's ability to generalize across different contexts. For more details on the LVIS dataset, explore the [LVIS dataset documentation](#key-features). diff --git a/docs/en/datasets/detect/objects365.md b/docs/en/datasets/detect/objects365.md index a52829bf..a3ffcbe8 100644 --- a/docs/en/datasets/detect/objects365.md +++ b/docs/en/datasets/detect/objects365.md @@ -65,7 +65,7 @@ To train a YOLOv8n model on the Objects365 dataset for 100 epochs with an image The Objects365 dataset contains a diverse set of high-resolution images with objects from 365 categories, providing rich context for object detection tasks. Here are some examples of the images in the dataset: -![Dataset sample image](https://user-images.githubusercontent.com/26833433/238215467-caf757dd-0b87-4b0d-bb19-d94a547f7fbf.jpg) +![Dataset sample image](https://github.com/ultralytics/docs/releases/download/0/objects365-sample-image.avif) - **Objects365**: This image demonstrates an example of object detection, where objects are annotated with bounding boxes. The dataset provides a wide range of images to facilitate the development of models for this task. diff --git a/docs/en/datasets/detect/open-images-v7.md b/docs/en/datasets/detect/open-images-v7.md index fe57d350..41e40d76 100644 --- a/docs/en/datasets/detect/open-images-v7.md +++ b/docs/en/datasets/detect/open-images-v7.md @@ -29,7 +29,7 @@ keywords: Open Images V7, Google dataset, computer vision, YOLOv8 models, object | [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-oiv7.pt) | 640 | 34.9 | 596.9 | 2.43 | 44.1 | 167.4 | | [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-oiv7.pt) | 640 | 36.3 | 860.6 | 3.56 | 68.7 | 260.6 | -![Open Images V7 classes visual](https://user-images.githubusercontent.com/26833433/258660358-2dc07771-ec08-4d11-b24a-f66e07550050.png) +![Open Images V7 classes visual](https://github.com/ultralytics/docs/releases/download/0/open-images-v7-classes-visual.avif) ## Key Features @@ -105,7 +105,7 @@ To train a YOLOv8n model on the Open Images V7 dataset for 100 epochs with an im Illustrations of the dataset help provide insights into its richness: -![Dataset sample image](https://storage.googleapis.com/openimages/web/images/oidv7_all-in-one_example_ab.jpg) +![Dataset sample image](https://github.com/ultralytics/docs/releases/download/0/oidv7-all-in-one-example-ab.avif) - **Open Images V7**: This image exemplifies the depth and detail of annotations available, including bounding boxes, relationships, and segmentation masks. diff --git a/docs/en/datasets/detect/roboflow-100.md b/docs/en/datasets/detect/roboflow-100.md index 541f8e8e..f2759123 100644 --- a/docs/en/datasets/detect/roboflow-100.md +++ b/docs/en/datasets/detect/roboflow-100.md @@ -9,7 +9,7 @@ keywords: Roboflow 100, Ultralytics, object detection, dataset, benchmarking, ma Roboflow 100, developed by [Roboflow](https://roboflow.com/?ref=ultralytics) and sponsored by Intel, is a groundbreaking [object detection](../../tasks/detect.md) benchmark. It includes 100 diverse datasets sampled from over 90,000 public datasets. This benchmark is designed to test the adaptability of models to various domains, including healthcare, aerial imagery, and video games.

- Roboflow 100 Overview + Roboflow 100 Overview

## Key Features @@ -104,7 +104,7 @@ You can access it directly from the Roboflow 100 GitHub repository. In addition, Roboflow 100 consists of datasets with diverse images and videos captured from various angles and domains. Here's a look at examples of annotated images in the RF100 benchmark.

- Sample Data and Annotations + Sample Data and Annotations

The diversity in the Roboflow 100 benchmark that can be seen above is a significant advancement from traditional benchmarks which often focus on optimizing a single metric within a limited domain. diff --git a/docs/en/datasets/detect/signature.md b/docs/en/datasets/detect/signature.md index bf62a6a6..db82a597 100644 --- a/docs/en/datasets/detect/signature.md +++ b/docs/en/datasets/detect/signature.md @@ -79,7 +79,7 @@ To train a YOLOv8n model on the signature detection dataset for 100 epochs with The signature detection dataset comprises a wide variety of images showcasing different document types and annotated signatures. Below are examples of images from the dataset, each accompanied by its corresponding annotations. -![Signature detection dataset sample image](https://github.com/RizwanMunawar/RizwanMunawar/assets/62513924/88a453da-3110-4835-9ae4-97bfb8b19046) +![Signature detection dataset sample image](https://github.com/ultralytics/docs/releases/download/0/signature-detection-mosaiced-sample.avif) - **Mosaiced Image**: Here, we present a training batch consisting of mosaiced dataset images. Mosaicing, a training technique, combines multiple images into one, enriching batch diversity. This method helps enhance the model's ability to generalize across different signature sizes, aspect ratios, and contexts. diff --git a/docs/en/datasets/detect/sku-110k.md b/docs/en/datasets/detect/sku-110k.md index d426d0f8..a16c4153 100644 --- a/docs/en/datasets/detect/sku-110k.md +++ b/docs/en/datasets/detect/sku-110k.md @@ -19,7 +19,7 @@ The [SKU-110k](https://github.com/eg4000/SKU110K_CVPR19) dataset is a collection Watch: How to Train YOLOv10 on SKU-110k Dataset using Ultralytics | Retail Dataset

-![Dataset sample image](https://user-images.githubusercontent.com/26833433/277141199-e7cdd803-237e-4b4a-9171-f95cba9388f9.jpg) +![Dataset sample image](https://github.com/ultralytics/docs/releases/download/0/densely-packed-retail-shelf.avif) ## Key Features @@ -78,7 +78,7 @@ To train a YOLOv8n model on the SKU-110K dataset for 100 epochs with an image si The SKU-110k dataset contains a diverse set of retail shelf images with densely packed objects, providing rich context for object detection tasks. Here are some examples of data from the dataset, along with their corresponding annotations: -![Dataset sample image](https://user-images.githubusercontent.com/26833433/277141197-b63e4aa5-12f6-4673-96a7-9a5207363c59.jpg) +![Dataset sample image](https://github.com/ultralytics/docs/releases/download/0/densely-packed-retail-shelf-1.avif) - **Densely packed retail shelf image**: This image demonstrates an example of densely packed objects in a retail shelf setting. Objects are annotated with bounding boxes and SKU category labels. diff --git a/docs/en/datasets/detect/visdrone.md b/docs/en/datasets/detect/visdrone.md index fab2fe80..88ddcb7c 100644 --- a/docs/en/datasets/detect/visdrone.md +++ b/docs/en/datasets/detect/visdrone.md @@ -74,7 +74,7 @@ To train a YOLOv8n model on the VisDrone dataset for 100 epochs with an image si The VisDrone dataset contains a diverse set of images and videos captured by drone-mounted cameras. Here are some examples of data from the dataset, along with their corresponding annotations: -![Dataset sample image](https://user-images.githubusercontent.com/26833433/238217600-df0b7334-4c9e-4c77-81a5-c70cd33429cc.jpg) +![Dataset sample image](https://github.com/ultralytics/docs/releases/download/0/visdrone-object-detection-sample.avif) - **Task 1**: Object detection in images - This image demonstrates an example of object detection in images, where objects are annotated with bounding boxes. The dataset provides a wide variety of images taken from different locations, environments, and densities to facilitate the development of models for this task. diff --git a/docs/en/datasets/detect/voc.md b/docs/en/datasets/detect/voc.md index 8fe76e6c..9efb5279 100644 --- a/docs/en/datasets/detect/voc.md +++ b/docs/en/datasets/detect/voc.md @@ -66,7 +66,7 @@ To train a YOLOv8n model on the VOC dataset for 100 epochs with an image size of The VOC dataset contains a diverse set of images with various object categories and complex scenes. Here are some examples of images from the dataset, along with their corresponding annotations: -![Dataset sample image](https://github.com/ultralytics/ultralytics/assets/26833433/7d4c18f4-774e-43f8-a5f3-9467cda7de4a) +![Dataset sample image](https://github.com/ultralytics/docs/releases/download/0/mosaiced-voc-dataset-sample.avif) - **Mosaiced Image**: This image demonstrates a training batch composed of mosaiced dataset images. Mosaicing is a technique used during training that combines multiple images into a single image to increase the variety of objects and scenes within each training batch. This helps improve the model's ability to generalize to different object sizes, aspect ratios, and contexts. diff --git a/docs/en/datasets/detect/xview.md b/docs/en/datasets/detect/xview.md index ca598e50..051ade1d 100644 --- a/docs/en/datasets/detect/xview.md +++ b/docs/en/datasets/detect/xview.md @@ -69,7 +69,7 @@ To train a model on the xView dataset for 100 epochs with an image size of 640, The xView dataset contains high-resolution satellite images with a diverse set of objects annotated using bounding boxes. Here are some examples of data from the dataset, along with their corresponding annotations: -![Dataset sample image](https://user-images.githubusercontent.com/26833433/277141257-ae6ba4de-5dcb-4c76-bc05-bc1e386361ba.jpg) +![Dataset sample image](https://github.com/ultralytics/docs/releases/download/0/overhead-imagery-object-detection.avif) - **Overhead Imagery**: This image demonstrates an example of object detection in overhead imagery, where objects are annotated with bounding boxes. The dataset provides high-resolution satellite images to facilitate the development of models for this task. diff --git a/docs/en/datasets/explorer/dashboard.md b/docs/en/datasets/explorer/dashboard.md index 3f23be00..ebf4a1b9 100644 --- a/docs/en/datasets/explorer/dashboard.md +++ b/docs/en/datasets/explorer/dashboard.md @@ -9,7 +9,7 @@ keywords: Ultralytics Explorer GUI, semantic search, vector similarity, SQL quer Explorer GUI is like a playground build using [Ultralytics Explorer API](api.md). It allows you to run semantic/vector similarity search, SQL queries and even search using natural language using our ask AI feature powered by LLMs.

- Explorer Dashboard Screenshot 1 + Explorer Dashboard Screenshot 1

@@ -41,19 +41,19 @@ Semantic search is a technique for finding similar images to a given image. It i For example: In this VOC Exploration dashboard, user selects a couple airplane images like this:

-Explorer Dashboard Screenshot 2 +Explorer Dashboard Screenshot 2

On performing similarity search, you should see a similar result:

-Explorer Dashboard Screenshot 3 +Explorer Dashboard Screenshot 3

## Ask AI This allows you to write how you want to filter your dataset using natural language. You don't have to be proficient in writing SQL queries. Our AI powered query generator will automatically do that under the hood. For example - you can say - "show me 100 images with exactly one person and 2 dogs. There can be other objects too" and it'll internally generate the query and show you those results. Here's an example output when asked to "Show 10 images with exactly 5 persons" and you'll see a result like this:

-Explorer Dashboard Screenshot 4 +Explorer Dashboard Screenshot 4

Note: This works using LLMs under the hood so the results are probabilistic and might get things wrong sometimes @@ -67,7 +67,7 @@ WHERE labels LIKE '%person%' AND labels LIKE '%dog%' ```

-Explorer Dashboard Screenshot 5 +Explorer Dashboard Screenshot 5

This is a Demo build using the Explorer API. You can use the API to build your own exploratory notebooks or scripts to get insights into your datasets. Learn more about the Explorer API [here](api.md). diff --git a/docs/en/datasets/explorer/index.md b/docs/en/datasets/explorer/index.md index 0ee612c1..c709aa2a 100644 --- a/docs/en/datasets/explorer/index.md +++ b/docs/en/datasets/explorer/index.md @@ -7,7 +7,7 @@ keywords: Ultralytics Explorer, CV datasets, semantic search, SQL queries, vecto # Ultralytics Explorer

- Ultralytics Explorer Screenshot 1 + Ultralytics Explorer Screenshot 1

Open In Colab @@ -56,7 +56,7 @@ yolo explorer You can set it like this - `yolo settings openai_api_key="..."`

- Ultralytics Explorer OpenAI Integration + Ultralytics Explorer OpenAI Integration

## FAQ diff --git a/docs/en/datasets/index.md b/docs/en/datasets/index.md index 1dabb621..2777cf38 100644 --- a/docs/en/datasets/index.md +++ b/docs/en/datasets/index.md @@ -24,7 +24,7 @@ Ultralytics provides support for various datasets to facilitate computer vision Create embeddings for your dataset, search for similar images, run SQL queries, perform semantic search and even search using natural language! You can get started with our GUI app or build your own using the API. Learn more [here](explorer/index.md).

-Ultralytics Explorer Screenshot +Ultralytics Explorer Screenshot

- Try the [GUI Demo](explorer/index.md) diff --git a/docs/en/datasets/obb/dota-v2.md b/docs/en/datasets/obb/dota-v2.md index 840ed0c1..240cf3eb 100644 --- a/docs/en/datasets/obb/dota-v2.md +++ b/docs/en/datasets/obb/dota-v2.md @@ -8,7 +8,7 @@ keywords: DOTA dataset, object detection, aerial images, oriented bounding boxes [DOTA](https://captain-whu.github.io/DOTA/index.html) stands as a specialized dataset, emphasizing object detection in aerial images. Originating from the DOTA series of datasets, it offers annotated images capturing a diverse array of aerial scenes with Oriented Bounding Boxes (OBB). -![DOTA classes visual](https://user-images.githubusercontent.com/26833433/259461765-72fdd0d8-266b-44a9-8199-199329bf5ca9.jpg) +![DOTA classes visual](https://github.com/ultralytics/docs/releases/download/0/dota-classes-visual.avif) ## Key Features @@ -126,7 +126,7 @@ To train a model on the DOTA v1 dataset, you can utilize the following code snip Having a glance at the dataset illustrates its depth: -![Dataset sample image](https://captain-whu.github.io/DOTA/images/instances-DOTA.jpg) +![Dataset sample image](https://github.com/ultralytics/docs/releases/download/0/instances-DOTA.avif) - **DOTA examples**: This snapshot underlines the complexity of aerial scenes and the significance of Oriented Bounding Box annotations, capturing objects in their natural orientation. diff --git a/docs/en/datasets/obb/dota8.md b/docs/en/datasets/obb/dota8.md index d1b22e55..2271978e 100644 --- a/docs/en/datasets/obb/dota8.md +++ b/docs/en/datasets/obb/dota8.md @@ -51,7 +51,7 @@ To train a YOLOv8n-obb model on the DOTA8 dataset for 100 epochs with an image s Here are some examples of images from the DOTA8 dataset, along with their corresponding annotations: -Dataset sample image +Dataset sample image - **Mosaiced Image**: This image demonstrates a training batch composed of mosaiced dataset images. Mosaicing is a technique used during training that combines multiple images into a single image to increase the variety of objects and scenes within each training batch. This helps improve the model's ability to generalize to different object sizes, aspect ratios, and contexts. diff --git a/docs/en/datasets/obb/index.md b/docs/en/datasets/obb/index.md index f7708a10..10631703 100644 --- a/docs/en/datasets/obb/index.md +++ b/docs/en/datasets/obb/index.md @@ -20,7 +20,7 @@ class_index x1 y1 x2 y2 x3 y3 x4 y4 Internally, YOLO processes losses and outputs in the `xywhr` format, which represents the bounding box's center point (xy), width, height, and rotation. -

OBB format examples

+

OBB format examples

An example of a `*.txt` label file for the above image, which contains an object of class `0` in OBB format, could look like: diff --git a/docs/en/datasets/pose/coco.md b/docs/en/datasets/pose/coco.md index 52fce86c..5adb09d3 100644 --- a/docs/en/datasets/pose/coco.md +++ b/docs/en/datasets/pose/coco.md @@ -8,7 +8,7 @@ keywords: COCO-Pose, pose estimation, dataset, keypoints, COCO Keypoints 2017, Y The [COCO-Pose](https://cocodataset.org/#keypoints-2017) dataset is a specialized version of the COCO (Common Objects in Context) dataset, designed for pose estimation tasks. It leverages the COCO Keypoints 2017 images and labels to enable the training of models like YOLO for pose estimation tasks. -![Pose sample image](https://user-images.githubusercontent.com/26833433/277141128-cd62d09e-1eb0-4d20-9938-c55239a5cb76.jpg) +![Pose sample image](https://github.com/ultralytics/docs/releases/download/0/pose-sample-image.avif) ## COCO-Pose Pretrained Models @@ -78,7 +78,7 @@ To train a YOLOv8n-pose model on the COCO-Pose dataset for 100 epochs with an im The COCO-Pose dataset contains a diverse set of images with human figures annotated with keypoints. Here are some examples of images from the dataset, along with their corresponding annotations: -![Dataset sample image](https://user-images.githubusercontent.com/26833433/239690150-a9dc0bd0-7ad9-4b78-a30f-189ed727ea0e.jpg) +![Dataset sample image](https://github.com/ultralytics/docs/releases/download/0/mosaiced-training-batch-6.avif) - **Mosaiced Image**: This image demonstrates a training batch composed of mosaiced dataset images. Mosaicing is a technique used during training that combines multiple images into a single image to increase the variety of objects and scenes within each training batch. This helps improve the model's ability to generalize to different object sizes, aspect ratios, and contexts. diff --git a/docs/en/datasets/pose/coco8-pose.md b/docs/en/datasets/pose/coco8-pose.md index 49295ac4..e5b2eb86 100644 --- a/docs/en/datasets/pose/coco8-pose.md +++ b/docs/en/datasets/pose/coco8-pose.md @@ -51,7 +51,7 @@ To train a YOLOv8n-pose model on the COCO8-Pose dataset for 100 epochs with an i Here are some examples of images from the COCO8-Pose dataset, along with their corresponding annotations: -Dataset sample image +Dataset sample image - **Mosaiced Image**: This image demonstrates a training batch composed of mosaiced dataset images. Mosaicing is a technique used during training that combines multiple images into a single image to increase the variety of objects and scenes within each training batch. This helps improve the model's ability to generalize to different object sizes, aspect ratios, and contexts. diff --git a/docs/en/datasets/pose/tiger-pose.md b/docs/en/datasets/pose/tiger-pose.md index d1e338cc..457e8fef 100644 --- a/docs/en/datasets/pose/tiger-pose.md +++ b/docs/en/datasets/pose/tiger-pose.md @@ -64,7 +64,7 @@ To train a YOLOv8n-pose model on the Tiger-Pose dataset for 100 epochs with an i Here are some examples of images from the Tiger-Pose dataset, along with their corresponding annotations: -Dataset sample image +Dataset sample image - **Mosaiced Image**: This image demonstrates a training batch composed of mosaiced dataset images. Mosaicing is a technique used during training that combines multiple images into a single image to increase the variety of objects and scenes within each training batch. This helps improve the model's ability to generalize to different object sizes, aspect ratios, and contexts. diff --git a/docs/en/datasets/segment/carparts-seg.md b/docs/en/datasets/segment/carparts-seg.md index 60890e06..d5799954 100644 --- a/docs/en/datasets/segment/carparts-seg.md +++ b/docs/en/datasets/segment/carparts-seg.md @@ -72,7 +72,7 @@ To train Ultralytics YOLOv8n model on the Carparts Segmentation dataset for 100 The Carparts Segmentation dataset includes a diverse array of images and videos taken from various perspectives. Below, you'll find examples of data from the dataset along with their corresponding annotations: -![Dataset sample image](https://github.com/RizwanMunawar/RizwanMunawar/assets/62513924/55da8284-a637-4858-aa1c-fc22d33a9c43) +![Dataset sample image](https://github.com/ultralytics/docs/releases/download/0/dataset-sample-image.avif) - This image illustrates object segmentation within a sample, featuring annotated bounding boxes with masks surrounding identified objects. The dataset consists of a varied set of images captured in various locations, environments, and densities, serving as a comprehensive resource for crafting models specific to this task. - This instance highlights the diversity and complexity inherent in the dataset, emphasizing the crucial role of high-quality data in computer vision tasks, particularly in the realm of car parts segmentation. diff --git a/docs/en/datasets/segment/coco.md b/docs/en/datasets/segment/coco.md index e02b6771..bb88a232 100644 --- a/docs/en/datasets/segment/coco.md +++ b/docs/en/datasets/segment/coco.md @@ -76,7 +76,7 @@ To train a YOLOv8n-seg model on the COCO-Seg dataset for 100 epochs with an imag COCO-Seg, like its predecessor COCO, contains a diverse set of images with various object categories and complex scenes. However, COCO-Seg introduces more detailed instance segmentation masks for each object in the images. Here are some examples of images from the dataset, along with their corresponding instance segmentation masks: -![Dataset sample image](https://user-images.githubusercontent.com/26833433/239690696-93fa8765-47a2-4b34-a6e5-516d0d1c725b.jpg) +![Dataset sample image](https://github.com/ultralytics/docs/releases/download/0/mosaiced-training-batch-3.avif) - **Mosaiced Image**: This image demonstrates a training batch composed of mosaiced dataset images. Mosaicing is a technique used during training that combines multiple images into a single image to increase the variety of objects and scenes within each training batch. This aids the model's ability to generalize to different object sizes, aspect ratios, and contexts. diff --git a/docs/en/datasets/segment/coco8-seg.md b/docs/en/datasets/segment/coco8-seg.md index bcca4a26..f22d6a68 100644 --- a/docs/en/datasets/segment/coco8-seg.md +++ b/docs/en/datasets/segment/coco8-seg.md @@ -51,7 +51,7 @@ To train a YOLOv8n-seg model on the COCO8-Seg dataset for 100 epochs with an ima Here are some examples of images from the COCO8-Seg dataset, along with their corresponding annotations: -Dataset sample image +Dataset sample image - **Mosaiced Image**: This image demonstrates a training batch composed of mosaiced dataset images. Mosaicing is a technique used during training that combines multiple images into a single image to increase the variety of objects and scenes within each training batch. This helps improve the model's ability to generalize to different object sizes, aspect ratios, and contexts. diff --git a/docs/en/datasets/segment/crack-seg.md b/docs/en/datasets/segment/crack-seg.md index 83f01987..5fa99dfb 100644 --- a/docs/en/datasets/segment/crack-seg.md +++ b/docs/en/datasets/segment/crack-seg.md @@ -61,7 +61,7 @@ To train Ultralytics YOLOv8n model on the Crack Segmentation dataset for 100 epo The Crack Segmentation dataset comprises a varied collection of images and videos captured from multiple perspectives. Below are instances of data from the dataset, accompanied by their respective annotations: -![Dataset sample image](https://github.com/RizwanMunawar/RizwanMunawar/assets/62513924/40ccc20a-9593-412f-b028-643d4a904d0e) +![Dataset sample image](https://github.com/ultralytics/docs/releases/download/0/crack-segmentation-sample.avif) - This image presents an example of image object segmentation, featuring annotated bounding boxes with masks outlining identified objects. The dataset includes a diverse array of images taken in different locations, environments, and densities, making it a comprehensive resource for developing models designed for this particular task. diff --git a/docs/en/datasets/segment/package-seg.md b/docs/en/datasets/segment/package-seg.md index 86fad9e9..bf88410f 100644 --- a/docs/en/datasets/segment/package-seg.md +++ b/docs/en/datasets/segment/package-seg.md @@ -61,7 +61,7 @@ To train Ultralytics YOLOv8n model on the Package Segmentation dataset for 100 e The Package Segmentation dataset comprises a varied collection of images and videos captured from multiple perspectives. Below are instances of data from the dataset, accompanied by their respective annotations: -![Dataset sample image](https://github.com/RizwanMunawar/RizwanMunawar/assets/62513924/55bdf5c8-4ae4-4824-8d08-63c15bdd9a92) +![Dataset sample image](https://github.com/ultralytics/docs/releases/download/0/dataset-sample-image-1.avif) - This image displays an instance of image object detection, featuring annotated bounding boxes with masks outlining recognized objects. The dataset incorporates a diverse collection of images taken in different locations, environments, and densities. It serves as a comprehensive resource for developing models specific to this task. - The example emphasizes the diversity and complexity present in the VisDrone dataset, underscoring the significance of high-quality sensor data for computer vision tasks involving drones. diff --git a/docs/en/guides/analytics.md b/docs/en/guides/analytics.md index 96cadb86..a29e6abe 100644 --- a/docs/en/guides/analytics.md +++ b/docs/en/guides/analytics.md @@ -12,9 +12,9 @@ This guide provides a comprehensive overview of three fundamental types of data ### Visual Samples -| Line Graph | Bar Plot | Pie Chart | -| :----------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------: | -| ![Line Graph](https://github.com/RizwanMunawar/RizwanMunawar/assets/62513924/eeabd90c-04fd-4e5b-aac9-c7777f892200) | ![Bar Plot](https://github.com/RizwanMunawar/RizwanMunawar/assets/62513924/c1da2d6a-99ff-43a8-b5dc-ca93127917f8) | ![Pie Chart](https://github.com/RizwanMunawar/RizwanMunawar/assets/62513924/9d8acce6-d9e4-4685-949d-cd4851483187) | +| Line Graph | Bar Plot | Pie Chart | +| :------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------: | +| ![Line Graph](https://github.com/ultralytics/docs/releases/download/0/line-graph.avif) | ![Bar Plot](https://github.com/ultralytics/docs/releases/download/0/bar-plot.avif) | ![Pie Chart](https://github.com/ultralytics/docs/releases/download/0/pie-chart.avif) | ### Why Graphs are Important diff --git a/docs/en/guides/azureml-quickstart.md b/docs/en/guides/azureml-quickstart.md index 0ffaa45d..92e3d837 100644 --- a/docs/en/guides/azureml-quickstart.md +++ b/docs/en/guides/azureml-quickstart.md @@ -33,7 +33,7 @@ Before you can get started, make sure you have access to an AzureML workspace. I From your AzureML workspace, select Compute > Compute instances > New, select the instance with the resources you need.

- Create Azure Compute Instance + Create Azure Compute Instance

## Quickstart from Terminal @@ -41,7 +41,7 @@ From your AzureML workspace, select Compute > Compute instances > New, select th Start your compute and open a Terminal:

- Open Terminal + Open Terminal

### Create virtualenv @@ -86,7 +86,7 @@ You can find more [instructions to use the Ultralytics CLI here](../quickstart.m Open the compute Terminal.

- Open Terminal + Open Terminal

From your compute terminal, you need to create a new ipykernel that will be used by your notebook to manage your dependencies: diff --git a/docs/en/guides/conda-quickstart.md b/docs/en/guides/conda-quickstart.md index 010d52c9..7afb202b 100644 --- a/docs/en/guides/conda-quickstart.md +++ b/docs/en/guides/conda-quickstart.md @@ -7,7 +7,7 @@ keywords: Ultralytics, Conda, setup, installation, environment, guide, machine l # Conda Quickstart Guide for Ultralytics

- Ultralytics Conda Package Visual + Ultralytics Conda Package Visual

This guide provides a comprehensive introduction to setting up a Conda environment for your Ultralytics projects. Conda is an open-source package and environment management system that offers an excellent alternative to pip for installing packages and dependencies. Its isolated environments make it particularly well-suited for data science and machine learning endeavors. For more details, visit the Ultralytics Conda package on [Anaconda](https://anaconda.org/conda-forge/ultralytics) and check out the Ultralytics feedstock repository for package updates on [GitHub](https://github.com/conda-forge/ultralytics-feedstock/). diff --git a/docs/en/guides/coral-edge-tpu-on-raspberry-pi.md b/docs/en/guides/coral-edge-tpu-on-raspberry-pi.md index ffc7654d..21b9589d 100644 --- a/docs/en/guides/coral-edge-tpu-on-raspberry-pi.md +++ b/docs/en/guides/coral-edge-tpu-on-raspberry-pi.md @@ -7,7 +7,7 @@ keywords: Coral Edge TPU, Raspberry Pi, YOLOv8, Ultralytics, TensorFlow Lite, ML # Coral Edge TPU on a Raspberry Pi with Ultralytics YOLOv8 🚀

- Raspberry Pi single board computer with USB Edge TPU accelerator + Raspberry Pi single board computer with USB Edge TPU accelerator

## What is a Coral Edge TPU? diff --git a/docs/en/guides/data-collection-and-annotation.md b/docs/en/guides/data-collection-and-annotation.md index 58a14ac9..2a7cb149 100644 --- a/docs/en/guides/data-collection-and-annotation.md +++ b/docs/en/guides/data-collection-and-annotation.md @@ -62,7 +62,7 @@ Depending on the specific requirements of a [computer vision task](../tasks/inde - **Keypoints**: Specific points marked within an image to identify locations of interest. Keypoints are used in tasks like pose estimation and facial landmark detection.

- Types of Data Annotation + Types of Data Annotation

### Common Annotation Formats @@ -91,7 +91,7 @@ Let's say you are ready to annotate now. There are several open-source tools ava - **[Labelme](https://github.com/labelmeai/labelme)**: A simple and easy-to-use tool that allows for quick annotation of images with polygons, making it ideal for straightforward tasks.

- LabelMe Overview + LabelMe Overview

These open-source tools are budget-friendly and provide a range of features to meet different annotation needs. @@ -105,7 +105,7 @@ Before you dive into annotating your data, there are a few more things to keep i It's important to understand the difference between accuracy and precision and how it relates to annotation. Accuracy refers to how close the annotated data is to the true values. It helps us measure how closely the labels reflect real-world scenarios. Precision indicates the consistency of annotations. It checks if you are giving the same label to the same object or feature throughout the dataset. High accuracy and precision lead to better-trained models by reducing noise and improving the model's ability to generalize from the training data.

- Example of Precision + Example of Precision

#### Identifying Outliers diff --git a/docs/en/guides/deepstream-nvidia-jetson.md b/docs/en/guides/deepstream-nvidia-jetson.md index 747dc4ac..dc0f77d8 100644 --- a/docs/en/guides/deepstream-nvidia-jetson.md +++ b/docs/en/guides/deepstream-nvidia-jetson.md @@ -8,7 +8,7 @@ keywords: Ultralytics, YOLOv8, NVIDIA Jetson, JetPack, AI deployment, embedded s This comprehensive guide provides a detailed walkthrough for deploying Ultralytics YOLOv8 on [NVIDIA Jetson](https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/) devices using DeepStream SDK and TensorRT. Here we use TensorRT to maximize the inference performance on the Jetson platform. -DeepStream on NVIDIA Jetson +DeepStream on NVIDIA Jetson !!! Note @@ -168,7 +168,7 @@ deepstream-app -c deepstream_app_config.txt It will take a long time to generate the TensorRT engine file before starting the inference. So please be patient. -
YOLOv8 with deepstream
+
YOLOv8 with deepstream
!!! Tip @@ -288,7 +288,7 @@ To set up multiple streams under a single deepstream application, you can do the deepstream-app -c deepstream_app_config.txt ``` -
Multistream setup
+
Multistream setup
## Benchmark Results diff --git a/docs/en/guides/defining-project-goals.md b/docs/en/guides/defining-project-goals.md index b241c7ea..3282cfe2 100644 --- a/docs/en/guides/defining-project-goals.md +++ b/docs/en/guides/defining-project-goals.md @@ -30,7 +30,7 @@ Let's walk through an example. Consider a computer vision project where you want to [estimate the speed of vehicles](./speed-estimation.md) on a highway. The core issue is that current speed monitoring methods are inefficient and error-prone due to outdated radar systems and manual processes. The project aims to develop a real-time computer vision system that can replace legacy [speed estimation](https://www.ultralytics.com/blog/ultralytics-yolov8-for-speed-estimation-in-computer-vision-projects) systems.

- Speed Estimation Using YOLOv8 + Speed Estimation Using YOLOv8

Primary users include traffic management authorities and law enforcement, while secondary stakeholders are highway planners and the public benefiting from safer roads. Key requirements involve evaluating budget, time, and personnel, as well as addressing technical needs like high-resolution cameras and real-time data processing. Additionally, regulatory constraints on privacy and data security must be considered. @@ -53,7 +53,7 @@ Your problem statement helps you conceptualize which computer vision task can so For example, if your problem is monitoring vehicle speeds on a highway, the relevant computer vision task is object tracking. [Object tracking](../modes/track.md) is suitable because it allows the system to continuously follow each vehicle in the video feed, which is crucial for accurately calculating their speeds.

- Example of Object Tracking + Example of Object Tracking

Other tasks, like [object detection](../tasks/detect.md), are not suitable as they don't provide continuous location or movement information. Once you've identified the appropriate computer vision task, it guides several critical aspects of your project, like model selection, dataset preparation, and model training approaches. @@ -82,7 +82,7 @@ Next, let's look at a few common discussion points in the community regarding co The most popular computer vision tasks include image classification, object detection, and image segmentation.

- Overview of Computer Vision Tasks + Overview of Computer Vision Tasks

For a detailed explanation of various tasks, please take a look at the Ultralytics Docs page on [YOLOv8 Tasks](../tasks/index.md). @@ -92,7 +92,7 @@ For a detailed explanation of various tasks, please take a look at the Ultralyti No, pre-trained models don't "remember" classes in the traditional sense. They learn patterns from massive datasets, and during custom training (fine-tuning), these patterns are adjusted for your specific task. The model's capacity is limited, and focusing on new information can overwrite some previous learnings.

- Overview of Transfer Learning + Overview of Transfer Learning

If you want to use the classes the model was pre-trained on, a practical approach is to use two models: one retains the original performance, and the other is fine-tuned for your specific task. This way, you can combine the outputs of both models. There are other options like freezing layers, using the pre-trained model as a feature extractor, and task-specific branching, but these are more complex solutions and require more expertise. diff --git a/docs/en/guides/distance-calculation.md b/docs/en/guides/distance-calculation.md index 3b0214f4..dbc15a4a 100644 --- a/docs/en/guides/distance-calculation.md +++ b/docs/en/guides/distance-calculation.md @@ -23,9 +23,9 @@ Measuring the gap between two objects is known as distance calculation within a ## Visuals -| Distance Calculation using Ultralytics YOLOv8 | -| :---------------------------------------------------------------------------------------------------------------------------------------------: | -| ![Ultralytics YOLOv8 Distance Calculation](https://github.com/RizwanMunawar/RizwanMunawar/assets/62513924/6b6b735d-3c49-4b84-a022-2bf6e3c72f8b) | +| Distance Calculation using Ultralytics YOLOv8 | +| :----------------------------------------------------------------------------------------------------------------------------------------------: | +| ![Ultralytics YOLOv8 Distance Calculation](https://github.com/ultralytics/docs/releases/download/0/ultralytics-yolov8-distance-calculation.avif) | ## Advantages of Distance Calculation? diff --git a/docs/en/guides/docker-quickstart.md b/docs/en/guides/docker-quickstart.md index 4d74a43b..90b86ed6 100644 --- a/docs/en/guides/docker-quickstart.md +++ b/docs/en/guides/docker-quickstart.md @@ -7,7 +7,7 @@ keywords: Ultralytics, Docker, Quickstart Guide, CPU support, GPU support, NVIDI # Docker Quickstart Guide for Ultralytics

- Ultralytics Docker Package Visual + Ultralytics Docker Package Visual

This guide serves as a comprehensive introduction to setting up a Docker environment for your Ultralytics projects. [Docker](https://docker.com/) is a platform for developing, shipping, and running applications in containers. It is particularly beneficial for ensuring that the software will always run the same, regardless of where it's deployed. For more details, visit the Ultralytics Docker repository on [Docker Hub](https://hub.docker.com/r/ultralytics/ultralytics). diff --git a/docs/en/guides/heatmaps.md b/docs/en/guides/heatmaps.md index 70a80bdd..4073f453 100644 --- a/docs/en/guides/heatmaps.md +++ b/docs/en/guides/heatmaps.md @@ -29,10 +29,10 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult ## Real World Applications -| Transportation | Retail | -| :---------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------: | -| ![Ultralytics YOLOv8 Transportation Heatmap](https://github.com/RizwanMunawar/ultralytics/assets/62513924/288d7053-622b-4452-b4e4-1f41aeb764aa) | ![Ultralytics YOLOv8 Retail Heatmap](https://github.com/RizwanMunawar/ultralytics/assets/62513924/edef75ad-50a7-4c0a-be4a-a66cdfc12802) | -| Ultralytics YOLOv8 Transportation Heatmap | Ultralytics YOLOv8 Retail Heatmap | +| Transportation | Retail | +| :--------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------: | +| ![Ultralytics YOLOv8 Transportation Heatmap](https://github.com/ultralytics/docs/releases/download/0/ultralytics-yolov8-transportation-heatmap.avif) | ![Ultralytics YOLOv8 Retail Heatmap](https://github.com/ultralytics/docs/releases/download/0/ultralytics-yolov8-retail-heatmap.avif) | +| Ultralytics YOLOv8 Transportation Heatmap | Ultralytics YOLOv8 Retail Heatmap | !!! tip "Heatmap Configuration" diff --git a/docs/en/guides/hyperparameter-tuning.md b/docs/en/guides/hyperparameter-tuning.md index f3b85305..09159565 100644 --- a/docs/en/guides/hyperparameter-tuning.md +++ b/docs/en/guides/hyperparameter-tuning.md @@ -20,7 +20,7 @@ Hyperparameters are high-level, structural settings for the algorithm. They are - **Architecture Specifics**: Such as channel counts, number of layers, types of activation functions, etc.

- Hyperparameter Tuning Visual + Hyperparameter Tuning Visual

For a full list of augmentation hyperparameters used in YOLOv8 please refer to the [configurations page](../usage/cfg.md#augmentation-settings). @@ -157,7 +157,7 @@ This is a plot displaying fitness (typically a performance metric like AP50) aga - **Usage**: Performance visualization

- Hyperparameter Tuning Fitness vs Iteration + Hyperparameter Tuning Fitness vs Iteration

#### tune_results.csv @@ -182,7 +182,7 @@ This file contains scatter plots generated from `tune_results.csv`, helping you - **Usage**: Exploratory data analysis

- Hyperparameter Tuning Scatter Plots + Hyperparameter Tuning Scatter Plots

#### weights/ diff --git a/docs/en/guides/instance-segmentation-and-tracking.md b/docs/en/guides/instance-segmentation-and-tracking.md index a26c2994..54127140 100644 --- a/docs/en/guides/instance-segmentation-and-tracking.md +++ b/docs/en/guides/instance-segmentation-and-tracking.md @@ -29,10 +29,10 @@ There are two types of instance segmentation tracking available in the Ultralyti ## Samples -| Instance Segmentation | Instance Segmentation + Object Tracking | -| :-------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------: | -| ![Ultralytics Instance Segmentation](https://github.com/RizwanMunawar/ultralytics/assets/62513924/d4ad3499-1f33-4871-8fbc-1be0b2643aa2) | ![Ultralytics Instance Segmentation with Object Tracking](https://github.com/RizwanMunawar/ultralytics/assets/62513924/2e5c38cc-fd5c-4145-9682-fa94ae2010a0) | -| Ultralytics Instance Segmentation 😍 | Ultralytics Instance Segmentation with Object Tracking 🔥 | +| Instance Segmentation | Instance Segmentation + Object Tracking | +| :----------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| ![Ultralytics Instance Segmentation](https://github.com/ultralytics/docs/releases/download/0/ultralytics-instance-segmentation.avif) | ![Ultralytics Instance Segmentation with Object Tracking](https://github.com/ultralytics/docs/releases/download/0/ultralytics-instance-segmentation-object-tracking.avif) | +| Ultralytics Instance Segmentation 😍 | Ultralytics Instance Segmentation with Object Tracking 🔥 | !!! Example "Instance Segmentation and Tracking" diff --git a/docs/en/guides/isolating-segmentation-objects.md b/docs/en/guides/isolating-segmentation-objects.md index 0c700b3b..9f099c8c 100644 --- a/docs/en/guides/isolating-segmentation-objects.md +++ b/docs/en/guides/isolating-segmentation-objects.md @@ -9,7 +9,7 @@ keywords: Ultralytics, segmentation, object isolation, Predict Mode, YOLOv8, mac After performing the [Segment Task](../tasks/segment.md), it's sometimes desirable to extract the isolated objects from the inference results. This guide provides a generic recipe on how to accomplish this using the Ultralytics [Predict Mode](../modes/predict.md).

- Example Isolated Object Segmentation + Example Isolated Object Segmentation

## Recipe Walk Through @@ -162,7 +162,7 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab There are no additional steps required if keeping full size image.
- ![Example Full size Isolated Object Image Black Background](https://github.com/ultralytics/ultralytics/assets/62214284/845c00d0-52a6-4b1e-8010-4ba73e011b99){ width=240 } + ![Example Full size Isolated Object Image Black Background](https://github.com/ultralytics/docs/releases/download/0/full-size-isolated-object-black-background.avif){ width=240 }
Example full-size output
@@ -170,7 +170,7 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab Additional steps required to crop image to only include object region. - ![Example Crop Isolated Object Image Black Background](https://github.com/ultralytics/ultralytics/assets/62214284/103dbf90-c169-4f77-b791-76cdf09c6f22){ align="right" } + ![Example Crop Isolated Object Image Black Background](https://github.com/ultralytics/docs/releases/download/0/example-crop-isolated-object-image-black-background.avif){ align="right" } ```{ .py .annotate } # (1) Bounding box coordinates x1, y1, x2, y2 = c.boxes.xyxy.cpu().numpy().squeeze().astype(np.int32) @@ -208,7 +208,7 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab There are no additional steps required if keeping full size image.
- ![Example Full size Isolated Object Image No Background](https://github.com/ultralytics/ultralytics/assets/62214284/b1043ee0-369a-4019-941a-9447a9771042){ width=240 } + ![Example Full size Isolated Object Image No Background](https://github.com/ultralytics/docs/releases/download/0/example-full-size-isolated-object-image-no-background.avif){ width=240 }
Example full-size output + transparent background
@@ -216,7 +216,7 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab Additional steps required to crop image to only include object region. - ![Example Crop Isolated Object Image No Background](https://github.com/ultralytics/ultralytics/assets/62214284/5910244f-d1e1-44af-af7f-6dea4c688da8){ align="right" } + ![Example Crop Isolated Object Image No Background](https://github.com/ultralytics/docs/releases/download/0/example-crop-isolated-object-image-no-background.avif){ align="right" } ```{ .py .annotate } # (1) Bounding box coordinates x1, y1, x2, y2 = c.boxes.xyxy.cpu().numpy().squeeze().astype(np.int32) diff --git a/docs/en/guides/kfold-cross-validation.md b/docs/en/guides/kfold-cross-validation.md index 6fb2f386..a0a34533 100644 --- a/docs/en/guides/kfold-cross-validation.md +++ b/docs/en/guides/kfold-cross-validation.md @@ -11,7 +11,7 @@ keywords: Ultralytics, YOLO, K-Fold Cross Validation, object detection, sklearn, This comprehensive guide illustrates the implementation of K-Fold Cross Validation for object detection datasets within the Ultralytics ecosystem. We'll leverage the YOLO detection format and key Python libraries such as sklearn, pandas, and PyYaml to guide you through the necessary setup, the process of generating feature vectors, and the execution of a K-Fold dataset split.

- K-Fold Cross Validation Overview + K-Fold Cross Validation Overview

Whether your project involves the Fruit Detection dataset or a custom data source, this tutorial aims to help you comprehend and apply K-Fold Cross Validation to bolster the reliability and robustness of your machine learning models. While we're applying `k=5` folds for this tutorial, keep in mind that the optimal number of folds can vary depending on your dataset and the specifics of your project. diff --git a/docs/en/guides/model-deployment-practices.md b/docs/en/guides/model-deployment-practices.md index f6375ddd..fae69cbd 100644 --- a/docs/en/guides/model-deployment-practices.md +++ b/docs/en/guides/model-deployment-practices.md @@ -49,7 +49,7 @@ Optimizing your computer vision model helps it runs efficiently, especially when Pruning reduces the size of the model by removing weights that contribute little to the final output. It makes the model smaller and faster without significantly affecting accuracy. Pruning involves identifying and eliminating unnecessary parameters, resulting in a lighter model that requires less computational power. It is particularly useful for deploying models on devices with limited resources.

- Model Pruning Overview + Model Pruning Overview

### Model Quantization @@ -65,7 +65,7 @@ Quantization converts the model's weights and activations from high precision (l Knowledge distillation involves training a smaller, simpler model (the student) to mimic the outputs of a larger, more complex model (the teacher). The student model learns to approximate the teacher's predictions, resulting in a compact model that retains much of the teacher's accuracy. This technique is beneficial for creating efficient models suitable for deployment on edge devices with constrained resources.

- Knowledge Distillation Overview + Knowledge Distillation Overview

## Troubleshooting Deployment Issues diff --git a/docs/en/guides/model-evaluation-insights.md b/docs/en/guides/model-evaluation-insights.md index 975cc1b9..feb31ad3 100644 --- a/docs/en/guides/model-evaluation-insights.md +++ b/docs/en/guides/model-evaluation-insights.md @@ -27,7 +27,7 @@ _Quick Tip:_ When running inferences, if you aren't seeing any predictions and y Intersection over Union (IoU) is a metric in object detection that measures how well the predicted bounding box overlaps with the ground truth bounding box. IoU values range from 0 to 1, where one stands for a perfect match. IoU is essential because it measures how closely the predicted boundaries match the actual object boundaries.

- Intersection over Union Overview + Intersection over Union Overview

### Mean Average Precision @@ -42,7 +42,7 @@ Let's focus on two specific mAP metrics: Other mAP metrics include mAP@0.75, which uses a stricter IoU threshold of 0.75, and mAP@small, medium, and large, which evaluate precision across objects of different sizes.

- Mean Average Precision Overview + Mean Average Precision Overview

## Evaluating YOLOv8 Model Performance diff --git a/docs/en/guides/model-monitoring-and-maintenance.md b/docs/en/guides/model-monitoring-and-maintenance.md index a301cf16..7864c66c 100644 --- a/docs/en/guides/model-monitoring-and-maintenance.md +++ b/docs/en/guides/model-monitoring-and-maintenance.md @@ -40,7 +40,7 @@ You can use automated monitoring tools to make it easier to monitor models after The three tools introduced above, Evidently AI, Prometheus, and Grafana, can work together seamlessly as a fully open-source ML monitoring solution that is ready for production. Evidently AI is used to collect and calculate metrics, Prometheus stores these metrics, and Grafana displays them and sets up alerts. While there are many other tools available, this setup is an exciting open-source option that provides robust capabilities for monitoring and maintaining your models.

- Overview of Open Source Model Monitoring Tools + Overview of Open Source Model Monitoring Tools

### Anomaly Detection and Alert Systems @@ -62,7 +62,7 @@ When you are setting up your alert systems, keep these best practices in mind: Data drift detection is a concept that helps identify when the statistical properties of the input data change over time, which can degrade model performance. Before you decide to retrain or adjust your models, this technique helps spot that there is an issue. Data drift deals with changes in the overall data landscape over time, while anomaly detection focuses on identifying rare or unexpected data points that may require immediate attention.

- Data Drift Detection Overview + Data Drift Detection Overview

Here are several methods to detect data drift: @@ -82,7 +82,7 @@ Model maintenance is crucial to keep computer vision models accurate and relevan Once a model is deployed, while monitoring, you may notice changes in data patterns or performance, indicating model drift. Regular updates and re-training become essential parts of model maintenance to ensure the model can handle new patterns and scenarios. There are a few techniques you can use based on how your data is changing.

- Computer Vision Model Drift Overview + Computer Vision Model Drift Overview

For example, if the data is changing gradually over time, incremental learning is a good approach. Incremental learning involves updating the model with new data without completely retraining it from scratch, saving computational resources and time. However, if the data has changed drastically, a periodic full re-training might be a better option to ensure the model does not overfit on the new data while losing track of older patterns. @@ -94,7 +94,7 @@ Regardless of the method, validation and testing are a must after updates. It is The frequency of retraining your computer vision model depends on data changes and model performance. Retrain your model whenever you observe a significant performance drop or detect data drift. Regular evaluations can help determine the right retraining schedule by testing the model against new data. Monitoring performance metrics and data patterns lets you decide if your model needs more frequent updates to maintain accuracy.

- When to Retrain Overview + When to Retrain Overview

## Documentation diff --git a/docs/en/guides/model-testing.md b/docs/en/guides/model-testing.md index d5ab89a1..718d1d11 100644 --- a/docs/en/guides/model-testing.md +++ b/docs/en/guides/model-testing.md @@ -88,7 +88,7 @@ Underfitting occurs when your model can't capture the underlying patterns in the The key is to find a balance between overfitting and underfitting. Ideally, a model should perform well on both training and validation datasets. Regularly monitoring your model's performance through metrics and visual inspections, along with applying the right strategies, can help you achieve the best results.

- Overfitting and Underfitting Overview + Overfitting and Underfitting Overview

## Data Leakage in Computer Vision and How to Avoid It diff --git a/docs/en/guides/model-training-tips.md b/docs/en/guides/model-training-tips.md index ea369d2d..20aaefa7 100644 --- a/docs/en/guides/model-training-tips.md +++ b/docs/en/guides/model-training-tips.md @@ -19,7 +19,7 @@ A computer vision model is trained by adjusting its internal parameters to minim During training, the model iteratively makes predictions, calculates errors, and updates its parameters through a process called backpropagation. In this process, the model adjusts its internal parameters (weights and biases) to reduce the errors. By repeating this cycle many times, the model gradually improves its accuracy. Over time, it learns to recognize complex patterns such as shapes, colors, and textures.

- What is Backpropagation? + What is Backpropagation?

This learning process makes it possible for the computer vision model to perform various [tasks](../tasks/index.md), including [object detection](../tasks/detect.md), [instance segmentation](../tasks/segment.md), and [image classification](../tasks/classify.md). The ultimate goal is to create a model that can generalize its learning to new, unseen images so that it can accurately understand visual data in real-world applications. @@ -64,7 +64,7 @@ Caching can be controlled when training YOLOv8 using the `cache` parameter: Mixed precision training uses both 16-bit (FP16) and 32-bit (FP32) floating-point types. The strengths of both FP16 and FP32 are leveraged by using FP16 for faster computation and FP32 to maintain precision where needed. Most of the neural network's operations are done in FP16 to benefit from faster computation and lower memory usage. However, a master copy of the model's weights is kept in FP32 to ensure accuracy during the weight update steps. You can handle larger models or larger batch sizes within the same hardware constraints.

- Mixed Precision Training Overview + Mixed Precision Training Overview

To implement mixed precision training, you'll need to modify your training scripts and ensure your hardware (like GPUs) supports it. Many modern deep learning frameworks, such as Tensorflow, offer built-in support for mixed precision. @@ -99,7 +99,7 @@ Early stopping is a valuable technique for optimizing model training. By monitor The process involves setting a patience parameter that determines how many epochs to wait for an improvement in validation metrics before stopping training. If the model's performance does not improve within these epochs, training is stopped to avoid wasting time and resources.

- Early Stopping Overview + Early Stopping Overview

For YOLOv8, you can enable early stopping by setting the patience parameter in your training configuration. For example, `patience=5` means training will stop if there's no improvement in validation metrics for 5 consecutive epochs. Using this method ensures the training process remains efficient and achieves optimal performance without excessive computation. diff --git a/docs/en/guides/nvidia-jetson.md b/docs/en/guides/nvidia-jetson.md index a361121a..504bfa90 100644 --- a/docs/en/guides/nvidia-jetson.md +++ b/docs/en/guides/nvidia-jetson.md @@ -19,7 +19,7 @@ This comprehensive guide provides a detailed walkthrough for deploying Ultralyti Watch: How to Setup NVIDIA Jetson with Ultralytics YOLOv8

-NVIDIA Jetson Ecosystem +NVIDIA Jetson Ecosystem !!! Note @@ -287,7 +287,7 @@ YOLOv8 benchmarks were run by the Ultralytics team on 10 different model formats Even though all model exports are working with NVIDIA Jetson, we have only included **PyTorch, TorchScript, TensorRT** for the comparison chart below because, they make use of the GPU on the Jetson and are guaranteed to produce the best results. All the other exports only utilize the CPU and the performance is not as good as the above three. You can find benchmarks for all exports in the section after this chart.
- NVIDIA Jetson Ecosystem + NVIDIA Jetson Ecosystem
### Detailed Comparison Table @@ -431,7 +431,7 @@ When using NVIDIA Jetson, there are a couple of best practices to follow in orde jtop ``` -Jetson Stats +Jetson Stats ## Next Steps diff --git a/docs/en/guides/object-counting.md b/docs/en/guides/object-counting.md index 033b845f..00aa9174 100644 --- a/docs/en/guides/object-counting.md +++ b/docs/en/guides/object-counting.md @@ -41,10 +41,10 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly ## Real World Applications -| Logistics | Aquaculture | -| :-----------------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------: | -| ![Conveyor Belt Packets Counting Using Ultralytics YOLOv8](https://github.com/RizwanMunawar/ultralytics/assets/62513924/70e2d106-510c-4c6c-a57a-d34a765aa757) | ![Fish Counting in Sea using Ultralytics YOLOv8](https://github.com/RizwanMunawar/ultralytics/assets/62513924/c60d047b-3837-435f-8d29-bb9fc95d2191) | -| Conveyor Belt Packets Counting Using Ultralytics YOLOv8 | Fish Counting in Sea using Ultralytics YOLOv8 | +| Logistics | Aquaculture | +| :-----------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------: | +| ![Conveyor Belt Packets Counting Using Ultralytics YOLOv8](https://github.com/ultralytics/docs/releases/download/0/conveyor-belt-packets-counting.avif) | ![Fish Counting in Sea using Ultralytics YOLOv8](https://github.com/ultralytics/docs/releases/download/0/fish-counting-in-sea-using-ultralytics-yolov8.avif) | +| Conveyor Belt Packets Counting Using Ultralytics YOLOv8 | Fish Counting in Sea using Ultralytics YOLOv8 | !!! Example "Object Counting using YOLOv8 Example" diff --git a/docs/en/guides/object-cropping.md b/docs/en/guides/object-cropping.md index d314cee0..3efaba93 100644 --- a/docs/en/guides/object-cropping.md +++ b/docs/en/guides/object-cropping.md @@ -29,10 +29,10 @@ Object cropping with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly ## Visuals -| Airport Luggage | -| :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | -| ![Conveyor Belt at Airport Suitcases Cropping using Ultralytics YOLOv8](https://github.com/RizwanMunawar/RizwanMunawar/assets/62513924/648f46be-f233-4307-a8e5-046eea38d2e4) | -| Suitcases Cropping at airport conveyor belt using Ultralytics YOLOv8 | +| Airport Luggage | +| :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| ![Conveyor Belt at Airport Suitcases Cropping using Ultralytics YOLOv8](https://github.com/ultralytics/docs/releases/download/0/suitcases-cropping-airport-conveyor-belt.avif) | +| Suitcases Cropping at airport conveyor belt using Ultralytics YOLOv8 | !!! Example "Object Cropping using YOLOv8 Example" diff --git a/docs/en/guides/optimizing-openvino-latency-vs-throughput-modes.md b/docs/en/guides/optimizing-openvino-latency-vs-throughput-modes.md index b6886d5d..a9acfb12 100644 --- a/docs/en/guides/optimizing-openvino-latency-vs-throughput-modes.md +++ b/docs/en/guides/optimizing-openvino-latency-vs-throughput-modes.md @@ -6,7 +6,7 @@ keywords: Ultralytics YOLO, OpenVINO optimization, deep learning, model inferenc # Optimizing OpenVINO Inference for Ultralytics YOLO Models: A Comprehensive Guide -OpenVINO Ecosystem +OpenVINO Ecosystem ## Introduction diff --git a/docs/en/guides/parking-management.md b/docs/en/guides/parking-management.md index cc42fb9b..e25936fb 100644 --- a/docs/en/guides/parking-management.md +++ b/docs/en/guides/parking-management.md @@ -29,10 +29,10 @@ Parking management with [Ultralytics YOLOv8](https://github.com/ultralytics/ultr ## Real World Applications -| Parking Management System | Parking Management System | -| :-----------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------: | -| ![Parking lots Analytics Using Ultralytics YOLOv8](https://github.com/RizwanMunawar/RizwanMunawar/assets/62513924/e3d4bc3e-cf4a-4da9-b42e-0da55cc74ad6) | ![Parking management top view using Ultralytics YOLOv8](https://github.com/RizwanMunawar/RizwanMunawar/assets/62513924/fe186719-1aca-43c9-b388-1ded91280eb5) | -| Parking management Aerial View using Ultralytics YOLOv8 | Parking management Top View using Ultralytics YOLOv8 | +| Parking Management System | Parking Management System | +| :----------------------------------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| ![Parking lots Analytics Using Ultralytics YOLOv8](https://github.com/ultralytics/docs/releases/download/0/parking-management-aerial-view-ultralytics-yolov8.avif) | ![Parking management top view using Ultralytics YOLOv8](https://github.com/ultralytics/docs/releases/download/0/parking-management-top-view-ultralytics-yolov8.avif) | +| Parking management Aerial View using Ultralytics YOLOv8 | Parking management Top View using Ultralytics YOLOv8 | ## Parking Management System Code Workflow @@ -61,7 +61,7 @@ Parking management with [Ultralytics YOLOv8](https://github.com/ultralytics/ultr - After defining the parking areas with polygons, click `save` to store a JSON file with the data in your working directory. -![Ultralytics YOLOv8 Points Selection Demo](https://github.com/RizwanMunawar/RizwanMunawar/assets/62513924/72737b8a-0f0f-4efb-98ad-b917a0039535) +![Ultralytics YOLOv8 Points Selection Demo](https://github.com/ultralytics/docs/releases/download/0/ultralytics-yolov8-points-selection-demo.avif) ### Python Code for Parking Management diff --git a/docs/en/guides/preprocessing_annotated_data.md b/docs/en/guides/preprocessing_annotated_data.md index 8935d7d8..ef771a28 100644 --- a/docs/en/guides/preprocessing_annotated_data.md +++ b/docs/en/guides/preprocessing_annotated_data.md @@ -73,7 +73,7 @@ Here are some other benefits of data augmentation: Common augmentation techniques include flipping, rotation, scaling, and color adjustments. Several libraries, such as Albumentations, Imgaug, and TensorFlow's ImageDataGenerator, can generate these augmentations.

- Overview of Data Augmentations + Overview of Data Augmentations

With respect to YOLOv8, you can [augment your custom dataset](../modes/train.md) by modifying the dataset configuration file, a .yaml file. In this file, you can add an augmentation section with parameters that specify how you want to augment your data. @@ -123,7 +123,7 @@ Common tools for visualizations include: For a more advanced approach to EDA, you can use the Ultralytics Explorer tool. It offers robust capabilities for exploring computer vision datasets. By supporting semantic search, SQL queries, and vector similarity search, the tool makes it easy to analyze and understand your data. With Ultralytics Explorer, you can create embeddings for your dataset to find similar images, run SQL queries for detailed analysis, and perform semantic searches, all through a user-friendly graphical interface.

- Overview of Ultralytics Explorer + Overview of Ultralytics Explorer

## Reach Out and Connect diff --git a/docs/en/guides/queue-management.md b/docs/en/guides/queue-management.md index 84724cb3..ad599770 100644 --- a/docs/en/guides/queue-management.md +++ b/docs/en/guides/queue-management.md @@ -28,10 +28,10 @@ Queue management using [Ultralytics YOLOv8](https://github.com/ultralytics/ultra ## Real World Applications -| Logistics | Retail | -| :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------: | -| ![Queue management at airport ticket counter using Ultralytics YOLOv8](https://github.com/RizwanMunawar/RizwanMunawar/assets/62513924/10487e76-bf60-4a9c-a0f3-5a75a05fa7a3) | ![Queue monitoring in crowd using Ultralytics YOLOv8](https://github.com/RizwanMunawar/RizwanMunawar/assets/62513924/dcc6d2ca-5576-434d-83c6-e57fe07bc693) | -| Queue management at airport ticket counter Using Ultralytics YOLOv8 | Queue monitoring in crowd Ultralytics YOLOv8 | +| Logistics | Retail | +| :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------: | +| ![Queue management at airport ticket counter using Ultralytics YOLOv8](https://github.com/ultralytics/docs/releases/download/0/queue-management-airport-ticket-counter-ultralytics-yolov8.avif) | ![Queue monitoring in crowd using Ultralytics YOLOv8](https://github.com/ultralytics/docs/releases/download/0/queue-monitoring-crowd-ultralytics-yolov8.avif) | +| Queue management at airport ticket counter Using Ultralytics YOLOv8 | Queue monitoring in crowd Ultralytics YOLOv8 | !!! Example "Queue Management using YOLOv8 Example" diff --git a/docs/en/guides/raspberry-pi.md b/docs/en/guides/raspberry-pi.md index de3af4f0..1d4b1f9d 100644 --- a/docs/en/guides/raspberry-pi.md +++ b/docs/en/guides/raspberry-pi.md @@ -149,13 +149,13 @@ YOLOv8 benchmarks were run by the Ultralytics team on nine different model forma === "YOLOv8n"
- NVIDIA Jetson Ecosystem + NVIDIA Jetson Ecosystem
=== "YOLOv8s"
- NVIDIA Jetson Ecosystem + NVIDIA Jetson Ecosystem
### Detailed Comparison Table diff --git a/docs/en/guides/region-counting.md b/docs/en/guides/region-counting.md index 52805257..f2be9d45 100644 --- a/docs/en/guides/region-counting.md +++ b/docs/en/guides/region-counting.md @@ -29,10 +29,10 @@ keywords: object counting, regions, YOLOv8, computer vision, Ultralytics, effici ## Real World Applications -| Retail | Market Streets | -| :----------------------------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------: | -| ![People Counting in Different Region using Ultralytics YOLOv8](https://github.com/RizwanMunawar/ultralytics/assets/62513924/5ab3bbd7-fd12-4849-928e-5f294d6c3fcf) | ![Crowd Counting in Different Region using Ultralytics YOLOv8](https://github.com/RizwanMunawar/ultralytics/assets/62513924/e7c1aea7-474d-4d78-8d48-b50854ffe1ca) | -| People Counting in Different Region using Ultralytics YOLOv8 | Crowd Counting in Different Region using Ultralytics YOLOv8 | +| Retail | Market Streets | +| :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| ![People Counting in Different Region using Ultralytics YOLOv8](https://github.com/ultralytics/docs/releases/download/0/people-counting-different-region-ultralytics-yolov8.avif) | ![Crowd Counting in Different Region using Ultralytics YOLOv8](https://github.com/ultralytics/docs/releases/download/0/crowd-counting-different-region-ultralytics-yolov8.avif) | +| People Counting in Different Region using Ultralytics YOLOv8 | Crowd Counting in Different Region using Ultralytics YOLOv8 | ## Steps to Run diff --git a/docs/en/guides/ros-quickstart.md b/docs/en/guides/ros-quickstart.md index 23dd8da2..c0691ffa 100644 --- a/docs/en/guides/ros-quickstart.md +++ b/docs/en/guides/ros-quickstart.md @@ -48,7 +48,7 @@ In ROS, communication between nodes is facilitated through [messages](https://wi This guide has been tested using [this ROS environment](https://github.com/ambitious-octopus/rosbot_ros/tree/noetic), which is a fork of the [ROSbot ROS repository](https://github.com/husarion/rosbot_ros). This environment includes the Ultralytics YOLO package, a Docker container for easy setup, comprehensive ROS packages, and Gazebo worlds for rapid testing. It is designed to work with the [Husarion ROSbot 2 PRO](https://husarion.com/manuals/rosbot/). The code examples provided will work in any ROS Noetic/Melodic environment, including both simulation and real-world.

- Husarion ROSbot 2 PRO + Husarion ROSbot 2 PRO

### Dependencies Installation @@ -72,7 +72,7 @@ Apart from the ROS environment, you will need to install the following dependenc The `sensor_msgs/Image` [message type](https://docs.ros.org/en/api/sensor_msgs/html/msg/Image.html) is commonly used in ROS for representing image data. It contains fields for encoding, height, width, and pixel data, making it suitable for transmitting images captured by cameras or other sensors. Image messages are widely used in robotic applications for tasks such as visual perception, object detection, and navigation.

- Detection and Segmentation in ROS Gazebo + Detection and Segmentation in ROS Gazebo

### Image Step-by-Step Usage @@ -345,7 +345,7 @@ while True: ## Use Ultralytics with ROS `sensor_msgs/PointCloud2`

- Detection and Segmentation in ROS Gazebo + Detection and Segmentation in ROS Gazebo

The `sensor_msgs/PointCloud2` [message type](https://docs.ros.org/en/api/sensor_msgs/html/msg/PointCloud2.html) is a data structure used in ROS to represent 3D point cloud data. This message type is integral to robotic applications, enabling tasks such as 3D mapping, object recognition, and localization. @@ -510,7 +510,7 @@ for index, class_id in enumerate(classes): ```

- Point Cloud Segmentation with Ultralytics + Point Cloud Segmentation with Ultralytics

## FAQ diff --git a/docs/en/guides/sahi-tiled-inference.md b/docs/en/guides/sahi-tiled-inference.md index 24e239eb..5795ab64 100644 --- a/docs/en/guides/sahi-tiled-inference.md +++ b/docs/en/guides/sahi-tiled-inference.md @@ -9,7 +9,7 @@ keywords: YOLOv8, SAHI, Sliced Inference, Object Detection, Ultralytics, High-re Welcome to the Ultralytics documentation on how to use YOLOv8 with [SAHI](https://github.com/obss/sahi) (Slicing Aided Hyper Inference). This comprehensive guide aims to furnish you with all the essential knowledge you'll need to implement SAHI alongside YOLOv8. We'll deep-dive into what SAHI is, why sliced inference is critical for large-scale applications, and how to integrate these functionalities with YOLOv8 for enhanced object detection performance.

- SAHI Sliced Inference Overview + SAHI Sliced Inference Overview

## Introduction to SAHI @@ -51,8 +51,8 @@ Sliced Inference refers to the practice of subdividing a large or high-resolutio YOLOv8 with SAHI - YOLOv8 without SAHI - YOLOv8 with SAHI + YOLOv8 without SAHI + YOLOv8 with SAHI diff --git a/docs/en/guides/security-alarm-system.md b/docs/en/guides/security-alarm-system.md index 4e2c652a..78bc2a9c 100644 --- a/docs/en/guides/security-alarm-system.md +++ b/docs/en/guides/security-alarm-system.md @@ -6,7 +6,7 @@ keywords: YOLOv8, Security Alarm System, real-time object detection, Ultralytics # Security Alarm System Project Using Ultralytics YOLOv8 -Security Alarm System +Security Alarm System The Security Alarm System Project utilizing Ultralytics YOLOv8 integrates advanced computer vision capabilities to enhance security measures. YOLOv8, developed by Ultralytics, provides real-time object detection, allowing the system to identify and respond to potential security threats promptly. This project offers several advantages: @@ -175,7 +175,7 @@ That's it! When you execute the code, you'll receive a single notification on yo #### Email Received Sample -Email Received Sample +Email Received Sample ## FAQ diff --git a/docs/en/guides/speed-estimation.md b/docs/en/guides/speed-estimation.md index ee42bfe6..9e9ddce5 100644 --- a/docs/en/guides/speed-estimation.md +++ b/docs/en/guides/speed-estimation.md @@ -33,10 +33,10 @@ keywords: Ultralytics YOLOv8, speed estimation, object tracking, computer vision ## Real World Applications -| Transportation | Transportation | -| :-----------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------: | -| ![Speed Estimation on Road using Ultralytics YOLOv8](https://github.com/RizwanMunawar/ultralytics/assets/62513924/c8a0fd4a-d394-436d-8de3-d5b754755fc7) | ![Speed Estimation on Bridge using Ultralytics YOLOv8](https://github.com/RizwanMunawar/ultralytics/assets/62513924/cee10e02-b268-4304-b73a-5b9cb42da669) | -| Speed Estimation on Road using Ultralytics YOLOv8 | Speed Estimation on Bridge using Ultralytics YOLOv8 | +| Transportation | Transportation | +| :------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| ![Speed Estimation on Road using Ultralytics YOLOv8](https://github.com/ultralytics/docs/releases/download/0/speed-estimation-on-road-using-ultralytics-yolov8.avif) | ![Speed Estimation on Bridge using Ultralytics YOLOv8](https://github.com/ultralytics/docs/releases/download/0/speed-estimation-on-bridge-using-ultralytics-yolov8.avif) | +| Speed Estimation on Road using Ultralytics YOLOv8 | Speed Estimation on Bridge using Ultralytics YOLOv8 | !!! Example "Speed Estimation using YOLOv8 Example" diff --git a/docs/en/guides/steps-of-a-cv-project.md b/docs/en/guides/steps-of-a-cv-project.md index a1fbdb5e..3b98171d 100644 --- a/docs/en/guides/steps-of-a-cv-project.md +++ b/docs/en/guides/steps-of-a-cv-project.md @@ -40,7 +40,7 @@ Before discussing the details of each step involved in a computer vision project - Finally, you'd deploy your model into the real world and update it based on new insights and feedback.

- Computer Vision Project Steps Overview + Computer Vision Project Steps Overview

Now that we know what to expect, let's dive right into the steps and get your project moving forward. @@ -71,7 +71,7 @@ Depending on the objective, you might choose to select the model first or after Choosing between training from scratch or using transfer learning affects how you prepare your data. Training from scratch requires a diverse dataset to build the model's understanding from the ground up. Transfer learning, on the other hand, allows you to use a pre-trained model and adapt it with a smaller, more specific dataset. Also, choosing a specific model to train will determine how you need to prepare your data, such as resizing images or adding annotations, according to the model's specific requirements.

- Training From Scratch Vs. Using Transfer Learning + Training From Scratch Vs. Using Transfer Learning

Note: When choosing a model, consider its [deployment](./model-deployment-options.md) to ensure compatibility and performance. For example, lightweight models are ideal for edge computing due to their efficiency on resource-constrained devices. To learn more about the key points related to defining your project, read [our guide](./defining-project-goals.md) on defining your project's goals and selecting the right model. @@ -97,7 +97,7 @@ However, if you choose to collect images or take your own pictures, you'll need - **Image Segmentation:** You'll label each pixel in the image according to the object it belongs to, creating detailed object boundaries.

- Different Types of Image Annotation + Different Types of Image Annotation

[Data collection and annotation](./data-collection-and-annotation.md) can be a time-consuming manual effort. Annotation tools can help make this process easier. Here are some useful open annotation tools: [LabeI Studio](https://github.com/HumanSignal/label-studio), [CVAT](https://github.com/cvat-ai/cvat), and [Labelme](https://github.com/labelmeai/labelme). @@ -115,7 +115,7 @@ Here's how to split your data: After splitting your data, you can perform data augmentation by applying transformations like rotating, scaling, and flipping images to artificially increase the size of your dataset. Data augmentation makes your model more robust to variations and improves its performance on unseen images.

- Examples of Data Augmentations + Examples of Data Augmentations

Libraries like OpenCV, Albumentations, and TensorFlow offer flexible augmentation functions that you can use. Additionally, some libraries, such as Ultralytics, have [built-in augmentation settings](../modes/train.md) directly within its model training function, simplifying the process. @@ -123,7 +123,7 @@ Libraries like OpenCV, Albumentations, and TensorFlow offer flexible augmentatio To understand your data better, you can use tools like [Matplotlib](https://matplotlib.org/) or [Seaborn](https://seaborn.pydata.org/) to visualize the images and analyze their distribution and characteristics. Visualizing your data helps identify patterns, anomalies, and the effectiveness of your augmentation techniques. You can also use [Ultralytics Explorer](../datasets/explorer/index.md), a tool for exploring computer vision datasets with semantic search, SQL queries, and vector similarity search.

- The Ultralytics Explorer Tool + The Ultralytics Explorer Tool

By properly [understanding, splitting, and augmenting your data](./preprocessing_annotated_data.md), you can develop a well-trained, validated, and tested model that performs well in real-world applications. @@ -177,7 +177,7 @@ Once your model is deployed, it's important to continuously monitor its performa Monitoring tools can help you track key performance indicators (KPIs) and detect anomalies or drops in accuracy. By monitoring the model, you can be aware of model drift, where the model's performance declines over time due to changes in the input data. Periodically retrain the model with updated data to maintain accuracy and relevance.

- Model Monitoring + Model Monitoring

In addition to monitoring and maintenance, documentation is also key. Thoroughly document the entire process, including model architecture, training procedures, hyperparameters, data preprocessing steps, and any changes made during deployment and maintenance. Good documentation ensures reproducibility and makes future updates or troubleshooting easier. By effectively monitoring, maintaining, and documenting your model, you can ensure it remains accurate, reliable, and easy to manage over its lifecycle. diff --git a/docs/en/guides/streamlit-live-inference.md b/docs/en/guides/streamlit-live-inference.md index 69de0996..d6a35613 100644 --- a/docs/en/guides/streamlit-live-inference.md +++ b/docs/en/guides/streamlit-live-inference.md @@ -21,10 +21,10 @@ Streamlit makes it simple to build and deploy interactive web applications. Comb Watch: How to Use Streamlit with Ultralytics for Real-Time Computer Vision in Your Browser

-| Aquaculture | Animals husbandry | -| :---------------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------: | -| ![Fish Detection using Ultralytics YOLOv8](https://github.com/RizwanMunawar/RizwanMunawar/assets/62513924/ea6d7ece-cded-4db7-b810-1f8433df2c96) | ![Animals Detection using Ultralytics YOLOv8](https://github.com/RizwanMunawar/RizwanMunawar/assets/62513924/2e1f4781-60ab-4e72-b3e4-726c10cd223c) | -| Fish Detection using Ultralytics YOLOv8 | Animals Detection using Ultralytics YOLOv8 | +| Aquaculture | Animals husbandry | +| :----------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------: | +| ![Fish Detection using Ultralytics YOLOv8](https://github.com/ultralytics/docs/releases/download/0/fish-detection-ultralytics-yolov8.avif) | ![Animals Detection using Ultralytics YOLOv8](https://github.com/ultralytics/docs/releases/download/0/animals-detection-yolov8.avif) | +| Fish Detection using Ultralytics YOLOv8 | Animals Detection using Ultralytics YOLOv8 | ## Advantages of Live Inference diff --git a/docs/en/guides/view-results-in-terminal.md b/docs/en/guides/view-results-in-terminal.md index 7c770e34..c599f664 100644 --- a/docs/en/guides/view-results-in-terminal.md +++ b/docs/en/guides/view-results-in-terminal.md @@ -7,7 +7,7 @@ keywords: YOLO, inference results, VSCode terminal, sixel, display images, Linux # Viewing Inference Results in a Terminal

- Sixel example of image in Terminal + Sixel example of image in Terminal

Image from the [libsixel](https://saitoha.github.io/libsixel/) website. @@ -32,7 +32,7 @@ The VSCode compatible protocols for viewing images using the integrated terminal ```

- VSCode enable terminal images setting + VSCode enable terminal images setting

2. Install the `python-sixel` library in your virtual environment. This is a [fork](https://github.com/lubosz/python-sixel?tab=readme-ov-file) of the `PySixel` library, which is no longer maintained. @@ -93,7 +93,7 @@ The VSCode compatible protocols for viewing images using the integrated terminal ## Example Inference Results

- View Image in Terminal + View Image in Terminal

!!! danger diff --git a/docs/en/guides/vision-eye.md b/docs/en/guides/vision-eye.md index 98fccd07..9fc1e0a7 100644 --- a/docs/en/guides/vision-eye.md +++ b/docs/en/guides/vision-eye.md @@ -12,10 +12,10 @@ keywords: VisionEye, YOLOv8, Ultralytics, object mapping, object tracking, dista ## Samples -| VisionEye View | VisionEye View With Object Tracking | VisionEye View With Distance Calculation | -| :----------------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------: | -| ![VisionEye View Object Mapping using Ultralytics YOLOv8](https://github.com/RizwanMunawar/ultralytics/assets/62513924/7d593acc-2e37-41b0-ad0e-92b4ffae6647) | ![VisionEye View Object Mapping with Object Tracking using Ultralytics YOLOv8](https://github.com/RizwanMunawar/ultralytics/assets/62513924/fcd85952-390f-451e-8fb0-b82e943af89c) | ![VisionEye View with Distance Calculation using Ultralytics YOLOv8](https://github.com/RizwanMunawar/RizwanMunawar/assets/62513924/18c4dafe-a22e-4fa9-a7d4-2bb293562a95) | -| VisionEye View Object Mapping using Ultralytics YOLOv8 | VisionEye View Object Mapping with Object Tracking using Ultralytics YOLOv8 | VisionEye View with Distance Calculation using Ultralytics YOLOv8 | +| VisionEye View | VisionEye View With Object Tracking | VisionEye View With Distance Calculation | +| :----------------------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| ![VisionEye View Object Mapping using Ultralytics YOLOv8](https://github.com/ultralytics/docs/releases/download/0/visioneye-view-object-mapping-yolov8.avif) | ![VisionEye View Object Mapping with Object Tracking using Ultralytics YOLOv8](https://github.com/ultralytics/docs/releases/download/0/visioneye-object-mapping-with-tracking.avif) | ![VisionEye View with Distance Calculation using Ultralytics YOLOv8](https://github.com/ultralytics/docs/releases/download/0/visioneye-distance-calculation-yolov8.avif) | +| VisionEye View Object Mapping using Ultralytics YOLOv8 | VisionEye View Object Mapping with Object Tracking using Ultralytics YOLOv8 | VisionEye View with Distance Calculation using Ultralytics YOLOv8 | !!! Example "VisionEye Object Mapping using YOLOv8" diff --git a/docs/en/guides/workouts-monitoring.md b/docs/en/guides/workouts-monitoring.md index 419c5624..91405551 100644 --- a/docs/en/guides/workouts-monitoring.md +++ b/docs/en/guides/workouts-monitoring.md @@ -29,10 +29,10 @@ Monitoring workouts through pose estimation with [Ultralytics YOLOv8](https://gi ## Real World Applications -| Workouts Monitoring | Workouts Monitoring | -| :--------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------: | -| ![PushUps Counting](https://github.com/RizwanMunawar/ultralytics/assets/62513924/cf016a41-589f-420f-8a8c-2cc8174a16de) | ![PullUps Counting](https://github.com/RizwanMunawar/ultralytics/assets/62513924/cb20f316-fac2-4330-8445-dcf5ffebe329) | -| PushUps Counting | PullUps Counting | +| Workouts Monitoring | Workouts Monitoring | +| :------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------: | +| ![PushUps Counting](https://github.com/ultralytics/docs/releases/download/0/pushups-counting.avif) | ![PullUps Counting](https://github.com/ultralytics/docs/releases/download/0/pullups-counting.avif) | +| PushUps Counting | PullUps Counting | !!! Example "Workouts Monitoring Example" @@ -108,7 +108,7 @@ Monitoring workouts through pose estimation with [Ultralytics YOLOv8](https://gi ### KeyPoints Map -![keyPoints Order Ultralytics YOLOv8 Pose](https://github.com/ultralytics/ultralytics/assets/62513924/f45d8315-b59f-47b7-b9c8-c61af1ce865b) +![keyPoints Order Ultralytics YOLOv8 Pose](https://github.com/ultralytics/docs/releases/download/0/keypoints-order-ultralytics-yolov8-pose.avif) ### Arguments `AIGym` diff --git a/docs/en/guides/yolo-common-issues.md b/docs/en/guides/yolo-common-issues.md index 03521b44..849b44c4 100644 --- a/docs/en/guides/yolo-common-issues.md +++ b/docs/en/guides/yolo-common-issues.md @@ -7,7 +7,7 @@ keywords: YOLO, YOLOv8, troubleshooting, installation errors, model training, GP # Troubleshooting Common YOLO Issues

- YOLO Common Issues Image + YOLO Common Issues Image

## Introduction diff --git a/docs/en/guides/yolo-thread-safe-inference.md b/docs/en/guides/yolo-thread-safe-inference.md index 3b476757..c0866851 100644 --- a/docs/en/guides/yolo-thread-safe-inference.md +++ b/docs/en/guides/yolo-thread-safe-inference.md @@ -13,7 +13,7 @@ Running YOLO models in a multi-threaded environment requires careful considerati Python threads are a form of parallelism that allow your program to run multiple operations at once. However, Python's Global Interpreter Lock (GIL) means that only one thread can execute Python bytecode at a time.

- Single vs Multi-Thread Examples + Single vs Multi-Thread Examples

While this sounds like a limitation, threads can still provide concurrency, especially for I/O-bound operations or when using operations that release the GIL, like those performed by YOLO's underlying C libraries. diff --git a/docs/en/help/contributing.md b/docs/en/help/contributing.md index 0ba05f6f..a4c23e99 100644 --- a/docs/en/help/contributing.md +++ b/docs/en/help/contributing.md @@ -9,7 +9,7 @@ keywords: Ultralytics, YOLO, open-source, contribution, pull request, code of co Welcome! We're thrilled that you're considering contributing to our [Ultralytics](https://ultralytics.com) [open-source](https://github.com/ultralytics) projects. Your involvement not only helps enhance the quality of our repositories but also benefits the entire community. This guide provides clear guidelines and best practices to help you get started. -Ultralytics open-source contributors +Ultralytics open-source contributors ## Table of Contents diff --git a/docs/en/hub/app/android.md b/docs/en/hub/app/android.md index 847ae916..c3c19b0c 100644 --- a/docs/en/hub/app/android.md +++ b/docs/en/hub/app/android.md @@ -7,7 +7,7 @@ keywords: Ultralytics, Android app, real-time object detection, YOLO models, Ten # Ultralytics Android App: Real-time Object Detection with YOLO Models - Ultralytics HUB preview image + Ultralytics HUB preview image
Ultralytics GitHub diff --git a/docs/en/hub/app/index.md b/docs/en/hub/app/index.md index 7d1731d4..9266a0bd 100644 --- a/docs/en/hub/app/index.md +++ b/docs/en/hub/app/index.md @@ -7,7 +7,7 @@ keywords: Ultralytics HUB, YOLO models, mobile app, iOS, Android, hardware accel # Ultralytics HUB App - Ultralytics HUB preview image + Ultralytics HUB preview image
Ultralytics GitHub diff --git a/docs/en/hub/app/ios.md b/docs/en/hub/app/ios.md index 2bbeb465..5468633f 100644 --- a/docs/en/hub/app/ios.md +++ b/docs/en/hub/app/ios.md @@ -7,7 +7,7 @@ keywords: Ultralytics, iOS App, YOLO models, real-time object detection, Apple N # Ultralytics iOS App: Real-time Object Detection with YOLO Models - Ultralytics HUB preview image + Ultralytics HUB preview image
Ultralytics GitHub diff --git a/docs/en/hub/cloud-training.md b/docs/en/hub/cloud-training.md index ab22a767..9d09a18f 100644 --- a/docs/en/hub/cloud-training.md +++ b/docs/en/hub/cloud-training.md @@ -26,13 +26,13 @@ In order to train models using Ultralytics Cloud Training, you need to [upgrade] 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! -![Ultralytics HUB screenshot of the Train Model dialog with arrows pointing to the Cloud Training options and the Start Training button](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/cloud-training/hub_cloud_training_train_model_1.jpg) +![Ultralytics HUB screenshot of the Train Model dialog with arrows pointing to the Cloud Training options and the Start Training button](https://github.com/ultralytics/docs/releases/download/0/ultralytics-hub-train-model-dialog.avif) ??? 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. - ![Ultralytics HUB screenshot of the Model page with an arrow pointing to the Start Training card](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/cloud-training/hub_cloud_training_train_model_2.jpg) + ![Ultralytics HUB screenshot of the Model page with an arrow pointing to the Start Training card](https://github.com/ultralytics/docs/releases/download/0/hub-cloud-training-model-page-start-training.avif) 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. @@ -40,7 +40,7 @@ Most of the times, you will use the Epochs training. The number of epochs can be 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. - ![Ultralytics HUB screenshot of the Train Model dialog with an arrow pointing to the Top-Up button](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/cloud-training/hub_cloud_training_train_model_3.jpg) + ![Ultralytics HUB screenshot of the Train Model dialog with an arrow pointing to the Top-Up button](https://github.com/ultralytics/docs/releases/download/0/ultralytics-hub-train-model-dialog-top-up.avif) !!! note "Note" @@ -48,21 +48,21 @@ Most of the times, you will use the Epochs training. The number of epochs can be 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. - ![Ultralytics HUB screenshot of the Model page with an arrow pointing to the Resume Training button](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/cloud-training/hub_cloud_training_train_model_4.jpg) + ![Ultralytics HUB screenshot of the Model page with an arrow pointing to the Resume Training button](https://github.com/ultralytics/docs/releases/download/0/hub-cloud-training-resume-training-button.avif) 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). If you have enough [account balance](./pro.md#account-balance), you can use the [Account Balance](./pro.md#account-balance) payment method. -![Ultralytics HUB screenshot of the Train Model dialog with an arrow pointing to the Start Training button](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/cloud-training/hub_cloud_training_train_model_5.jpg) +![Ultralytics HUB screenshot of the Train Model dialog with an arrow pointing to the Start Training button](https://github.com/ultralytics/docs/releases/download/0/ultralytics-hub-train-model-start-training.avif) 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. -![Ultralytics HUB screenshot of the Train Model dialog with an arrow pointing to the Pay Now button](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/cloud-training/hub_cloud_training_train_model_6.jpg) +![Ultralytics HUB screenshot of the Train Model dialog with an arrow pointing to the Pay Now button](https://github.com/ultralytics/docs/releases/download/0/hub-cloud-training-train-model-pay-now-button.avif) 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. -![Ultralytics HUB screenshot of the Model page during the initialization process](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/cloud-training/hub_cloud_training_train_model_7.jpg) +![Ultralytics HUB screenshot of the Model page during the initialization process](https://github.com/ultralytics/docs/releases/download/0/model-page-initialization-process.avif) !!! note "Note" @@ -72,13 +72,13 @@ After the training session starts, you can monitor each step of the progress. If needed, you can stop the training by clicking on the **Stop Training** button. -![Ultralytics HUB screenshot of the Model page of a model that is currently training with an arrow pointing to the Stop Training button](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/cloud-training/hub_cloud_training_train_model_8.jpg) +![Ultralytics HUB screenshot of the Model page of a model that is currently training with an arrow pointing to the Stop Training button](https://github.com/ultralytics/docs/releases/download/0/model-page-training-stop-button.avif) !!! note "Note" You can resume training your model from the last checkpoint saved. - ![Ultralytics HUB screenshot of the Model page with an arrow pointing to the Resume Training button](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/cloud-training/hub_cloud_training_train_model_4.jpg) + ![Ultralytics HUB screenshot of the Model page with an arrow pointing to the Resume Training button](https://github.com/ultralytics/docs/releases/download/0/hub-cloud-training-resume-training-button.avif)