Fix gitignore to format Docs datasets (#16071)
Signed-off-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Glenn Jocher
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41 changed files with 767 additions and 744 deletions
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@ -115,7 +115,7 @@ To train a YOLOv8 model on the Carparts Segmentation dataset, you can follow the
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!!! Example "Train Example"
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=== "Python"
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```python
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from ultralytics import YOLO
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@ -138,6 +138,7 @@ For more details, refer to the [Training](../../modes/train.md) documentation.
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### What are some applications of Carparts Segmentation?
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Carparts Segmentation can be widely applied in various fields such as:
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- **Automotive quality control**
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- **Auto repair and maintenance**
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- **E-commerce cataloging**
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@ -155,6 +156,6 @@ The dataset configuration file for the Carparts Segmentation dataset, `carparts-
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### Why should I use the Carparts Segmentation Dataset?
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The Carparts Segmentation Dataset provides rich, annotated data essential for developing high-accuracy segmentation models in automotive computer vision. This dataset's diversity and detailed annotations improve model training, making it ideal for applications like vehicle maintenance automation, enhancing vehicle safety systems, and supporting autonomous driving technologies. Partnering with a robust dataset accelerates AI development and ensures better model performance.
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The Carparts Segmentation Dataset provides rich, annotated data essential for developing high-accuracy segmentation models in automotive computer vision. This dataset's diversity and detailed annotations improve model training, making it ideal for applications like vehicle maintenance automation, enhancing vehicle safety systems, and supporting autonomous driving technologies. Partnering with a robust dataset accelerates AI development and ensures better model performance.
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For more details, visit the [CarParts Segmentation Dataset Page](https://universe.roboflow.com/gianmarco-russo-vt9xr/car-seg-un1pm?ref=ultralytics).
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@ -11,7 +11,7 @@ The [COCO-Seg](https://cocodataset.org/#home) dataset, an extension of the COCO
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## COCO-Seg Pretrained Models
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| Model | size<br><sup>(pixels) | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
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|----------------------------------------------------------------------------------------------|-----------------------|----------------------|-----------------------|--------------------------------|-------------------------------------|--------------------|-------------------|
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| -------------------------------------------------------------------------------------------- | --------------------- | -------------------- | --------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
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| [YOLOv8n-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n-seg.pt) | 640 | 36.7 | 30.5 | 96.1 | 1.21 | 3.4 | 12.6 |
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| [YOLOv8s-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-seg.pt) | 640 | 44.6 | 36.8 | 155.7 | 1.47 | 11.8 | 42.6 |
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| [YOLOv8m-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-seg.pt) | 640 | 49.9 | 40.8 | 317.0 | 2.18 | 27.3 | 110.2 |
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@ -116,7 +116,7 @@ To train a YOLOv8n-seg model on the COCO-Seg dataset for 100 epochs with an imag
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!!! Example "Train Example"
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=== "Python"
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```python
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from ultralytics import YOLO
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@ -128,7 +128,7 @@ To train a YOLOv8n-seg model on the COCO-Seg dataset for 100 epochs with an imag
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```
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=== "CLI"
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```bash
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# Start training from a pretrained *.pt model
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yolo segment train data=coco-seg.yaml model=yolov8n-seg.pt epochs=100 imgsz=640
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@ -148,7 +148,7 @@ The COCO-Seg dataset includes several key features:
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The COCO-Seg dataset supports multiple pretrained YOLOv8 segmentation models with varying performance metrics. Here's a summary of the available models and their key metrics:
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| Model | size<br><sup>(pixels) | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
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|----------------------------------------------------------------------------------------------|-----------------------|----------------------|-----------------------|--------------------------------|-------------------------------------|--------------------|-------------------|
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| -------------------------------------------------------------------------------------------- | --------------------- | -------------------- | --------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
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| [YOLOv8n-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n-seg.pt) | 640 | 36.7 | 30.5 | 96.1 | 1.21 | 3.4 | 12.6 |
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| [YOLOv8s-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-seg.pt) | 640 | 44.6 | 36.8 | 155.7 | 1.47 | 11.8 | 42.6 |
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| [YOLOv8m-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-seg.pt) | 640 | 49.9 | 40.8 | 317.0 | 2.18 | 27.3 | 110.2 |
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@ -91,7 +91,7 @@ To train a **YOLOv8n-seg** model on the COCO8-Seg dataset for 100 epochs with an
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!!! Example "Train Example"
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=== "Python"
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```python
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from ultralytics import YOLO
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@ -103,7 +103,7 @@ To train a **YOLOv8n-seg** model on the COCO8-Seg dataset for 100 epochs with an
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```
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=== "CLI"
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```bash
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# Start training from a pretrained *.pt model
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yolo segment train data=coco8-seg.yaml model=yolov8n-seg.pt epochs=100 imgsz=640
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@ -44,20 +44,20 @@ The Ultralytics framework uses a YAML file format to define the dataset and mode
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```yaml
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# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
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path: ../datasets/coco8-seg # dataset root dir
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train: images/train # train images (relative to 'path') 4 images
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val: images/val # val images (relative to 'path') 4 images
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test: # test images (optional)
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path: ../datasets/coco8-seg # dataset root dir
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train: images/train # train images (relative to 'path') 4 images
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val: images/val # val images (relative to 'path') 4 images
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test: # test images (optional)
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# Classes (80 COCO classes)
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names:
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0: person
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1: bicycle
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2: car
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# ...
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77: teddy bear
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78: hair drier
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79: toothbrush
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0: person
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1: bicycle
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2: car
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# ...
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77: teddy bear
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78: hair drier
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79: toothbrush
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```
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The `train` and `val` fields specify the paths to the directories containing the training and validation images, respectively.
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@ -141,7 +141,7 @@ To auto-annotate your dataset using the Ultralytics framework, you can use the `
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```
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| Argument | Type | Description | Default |
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|--------------|-------------------------|-------------------------------------------------------------------------------------------------------------|----------------|
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| ------------ | ----------------------- | ----------------------------------------------------------------------------------------------------------- | -------------- |
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| `data` | `str` | Path to a folder containing images to be annotated. | `None` |
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| `det_model` | `str, optional` | Pre-trained YOLO detection model. Defaults to `'yolov8x.pt'`. | `'yolov8x.pt'` |
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| `sam_model` | `str, optional` | Pre-trained SAM segmentation model. Defaults to `'sam_b.pt'`. | `'sam_b.pt'` |
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@ -175,15 +175,15 @@ This script converts your COCO dataset annotations to the required YOLO format,
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To prepare a YAML file for training YOLO models with Ultralytics, you need to define the dataset paths and class names. Here's an example YAML configuration:
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```yaml
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path: ../datasets/coco8-seg # dataset root dir
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train: images/train # train images (relative to 'path')
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val: images/val # val images (relative to 'path')
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path: ../datasets/coco8-seg # dataset root dir
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train: images/train # train images (relative to 'path')
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val: images/val # val images (relative to 'path')
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names:
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0: person
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1: bicycle
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2: car
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# ...
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0: person
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1: bicycle
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2: car
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# ...
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```
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Ensure you update the paths and class names according to your dataset. For more information, check the [Dataset YAML Format](#dataset-yaml-format) section.
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@ -104,7 +104,7 @@ You can train an Ultralytics YOLOv8n model using both Python and CLI methods. Us
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!!! Example "Train Example"
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=== "Python"
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```python
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from ultralytics import YOLO
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@ -116,7 +116,7 @@ You can train an Ultralytics YOLOv8n model using both Python and CLI methods. Us
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```
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=== "CLI"
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```bash
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# Start training from a pretrained *.pt model
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yolo segment train data=package-seg.yaml model=yolov8n-seg.pt epochs=100 imgsz=640
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@ -127,6 +127,7 @@ Refer to the model [Training](../../modes/train.md) page for more details.
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### What are the components of the Package Segmentation Dataset, and how is it structured?
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The dataset is structured into three main components:
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- **Training set**: Contains 1920 images with annotations.
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- **Testing set**: Comprises 89 images with corresponding annotations.
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- **Validation set**: Includes 188 images with annotations.
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@ -139,6 +140,6 @@ Ultralytics YOLOv8 provides state-of-the-art accuracy and speed for real-time ob
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### How can I access and use the package-seg.yaml file for the Package Segmentation Dataset?
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The `package-seg.yaml` file is hosted on Ultralytics' GitHub repository and contains essential information about the dataset's paths, classes, and configuration. You can download it from [here](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/package-seg.yaml). This file is crucial for configuring your models to utilize the dataset efficiently.
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The `package-seg.yaml` file is hosted on Ultralytics' GitHub repository and contains essential information about the dataset's paths, classes, and configuration. You can download it from [here](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/package-seg.yaml). This file is crucial for configuring your models to utilize the dataset efficiently.
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For more insights and practical examples, explore our [Usage](https://docs.ultralytics.com/usage/python/) section.
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