fix(pre_commit): 🎨 auto format pre-commit hooks
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@@ -9,20 +9,20 @@ visualize, and augment datasets in Supervision.
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## Download Dataset
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In this tutorial, we will use a dataset from
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[Roboflow Universe](https://universe.roboflow.com/), a public repository of
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thousands of computer vision datasets. If you already have your dataset in
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[COCO](https://roboflow.com/formats/coco-json),
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[YOLO](https://roboflow.com/formats/yolov8-pytorch-txt),
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or [Pascal VOC](https://roboflow.com/formats/pascal-voc-xml) format, you can skip this
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In this tutorial, we will use a dataset from
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[Roboflow Universe](https://universe.roboflow.com/), a public repository of
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thousands of computer vision datasets. If you already have your dataset in
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[COCO](https://roboflow.com/formats/coco-json),
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[YOLO](https://roboflow.com/formats/yolov8-pytorch-txt),
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or [Pascal VOC](https://roboflow.com/formats/pascal-voc-xml) format, you can skip this
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section.
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```bash
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pip install roboflow
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```
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Next, log into your Roboflow account and download the dataset of your choice in the
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COCO, YOLO, or Pascal VOC format. You can customize the following code snippet with
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Next, log into your Roboflow account and download the dataset of your choice in the
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COCO, YOLO, or Pascal VOC format. You can customize the following code snippet with
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your workspace ID, project ID, and version number.
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=== "COCO"
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@@ -31,7 +31,7 @@ your workspace ID, project ID, and version number.
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import roboflow
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roboflow.login()
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rf = roboflow.Roboflow()
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project = rf.workspace(<WORKSPACE_ID>).project(<PROJECT_ID>)
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dataset = project.version(<PROJECT_VERSION>).download("coco")
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@@ -43,7 +43,7 @@ your workspace ID, project ID, and version number.
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import roboflow
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roboflow.login()
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rf = roboflow.Roboflow()
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project = rf.workspace(<WORKSPACE_ID>).project(<PROJECT_ID>)
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dataset = project.version(<PROJECT_VERSION>).download("yolov8")
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@@ -55,7 +55,7 @@ your workspace ID, project ID, and version number.
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import roboflow
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roboflow.login()
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rf = roboflow.Roboflow()
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project = rf.workspace(<WORKSPACE_ID>).project(<PROJECT_ID>)
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dataset = project.version(<PROJECT_VERSION>).download("voc")
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@@ -63,9 +63,9 @@ your workspace ID, project ID, and version number.
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## Load Dataset
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The Supervision library provides convenient functions to load datasets in various
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formats. If your dataset is already split into train, test, and valid subsets, you can
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load each of those as separate [`sv.DetectionDataset`](https://supervision.roboflow.com/latest/datasets/core/#supervision.dataset.core.DetectionDataset)
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The Supervision library provides convenient functions to load datasets in various
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formats. If your dataset is already split into train, test, and valid subsets, you can
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load each of those as separate [`sv.DetectionDataset`](https://supervision.roboflow.com/latest/datasets/core/#supervision.dataset.core.DetectionDataset)
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instances.
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=== "COCO"
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@@ -74,7 +74,7 @@ instances.
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```python
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import supervision as sv
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ds_train = sv.DetectionDataset.from_coco(
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images_directory_path=f"{dataset.location}/train",
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annotations_path=f"{dataset.location}/train/_annotations.coco.json",
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@@ -90,7 +90,7 @@ instances.
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ds_train.classes
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# ['person', 'bicycle', 'car', ...]
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len(ds_train), len(ds_valid), len(ds_test)
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# 800, 100, 100
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```
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@@ -120,7 +120,7 @@ instances.
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ds_train.classes
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# ['person', 'bicycle', 'car', ...]
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len(ds_train), len(ds_valid), len(ds_test)
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# 800, 100, 100
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```
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@@ -147,16 +147,16 @@ instances.
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ds_train.classes
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# ['person', 'bicycle', 'car', ...]
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len(ds_train), len(ds_valid), len(ds_test)
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# 800, 100, 100
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```
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## Split Dataset
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If your dataset is not already split into train, test, and valid subsets, you can
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easily do so using the [`sv.DetectionDataset.split`](https://supervision.roboflow.com/latest/datasets/core/#supervision.dataset.core.DetectionDataset.split)
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method. Let's assume we have a DetectionDataset named ds containing 1000 images. We
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If your dataset is not already split into train, test, and valid subsets, you can
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easily do so using the [`sv.DetectionDataset.split`](https://supervision.roboflow.com/latest/datasets/core/#supervision.dataset.core.DetectionDataset.split)
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method. Let's assume we have a DetectionDataset named ds containing 1000 images. We
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can split it as follows, ensuring a random shuffle of the data.
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@@ -179,7 +179,7 @@ len(ds_train), len(ds_valid), len(ds_test)
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There are two ways to loop over a `sv.DetectionDataset`:
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- using a direct [for loop](https://supervision.roboflow.com/latest/datasets/core/#supervision.dataset.core.DetectionDataset.__iter__)
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- using a direct [for loop](https://supervision.roboflow.com/latest/datasets/core/#supervision.dataset.core.DetectionDataset.__iter__)
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called on the `sv.DetectionDataset` instance
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- loading `sv.DetectionDataset` entries [by index](https://supervision.roboflow.com/latest/datasets/core/#supervision.dataset.core.DetectionDataset.__getitem__).
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@@ -191,7 +191,7 @@ ds = sv.DetectionDataset(...)
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# Option 1
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for image_path, image, annotations in ds:
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... # Process each image and its annotations
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# Option 2
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for idx in range(len(ds)):
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image_path, image, annotations = ds[idx]
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@@ -200,12 +200,12 @@ for idx in range(len(ds)):
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## Visualize Dataset
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The Supervision library provides tools for easily visualizing your detection dataset.
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You can create a grid of annotated images to quickly inspect your data and labels.
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First, initialize the [`sv.BoxAnnotator`](https://supervision.roboflow.com/latest/detection/annotators/#supervision.annotators.core.BoxAnnotator)
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and [`sv.LabelAnnotator`](https://supervision.roboflow.com/latest/detection/annotators/#supervision.annotators.core.LabelAnnotator).
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Then, iterate through a subset of the dataset (e.g., the first 25 images), drawing
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bounding boxes and class labels on each image. Finally, combine the annotated images
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The Supervision library provides tools for easily visualizing your detection dataset.
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You can create a grid of annotated images to quickly inspect your data and labels.
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First, initialize the [`sv.BoxAnnotator`](https://supervision.roboflow.com/latest/detection/annotators/#supervision.annotators.core.BoxAnnotator)
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and [`sv.LabelAnnotator`](https://supervision.roboflow.com/latest/detection/annotators/#supervision.annotators.core.LabelAnnotator).
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Then, iterate through a subset of the dataset (e.g., the first 25 images), drawing
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bounding boxes and class labels on each image. Finally, combine the annotated images
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into a grid for display.
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```python
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