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Reformat Markdown code blocks (#12795)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Glenn Jocher 2024-05-18 18:58:06 +02:00 • committed by GitHub
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docs/en/usage/python.md
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@ -27,22 +27,22 @@ For example, users can load a model, train it, evaluate its performance on a val
from ultralytics import YOLO
# Create a new YOLO model from scratch
model = YOLO('yolov8n.yaml')
model = YOLO("yolov8n.yaml")
# Load a pretrained YOLO model (recommended for training)
model = YOLO('yolov8n.pt')
model = YOLO("yolov8n.pt")
# Train the model using the 'coco8.yaml' dataset for 3 epochs
results = model.train(data='coco8.yaml', epochs=3)
results = model.train(data="coco8.yaml", epochs=3)
# Evaluate the model's performance on the validation set
results = model.val()
# Perform object detection on an image using the model
results = model('https://ultralytics.com/images/bus.jpg')
results = model("https://ultralytics.com/images/bus.jpg")
# Export the model to ONNX format
success = model.export(format='onnx')
success = model.export(format="onnx")
```
## [Train](../modes/train.md)
@ -56,7 +56,7 @@ Train mode is used for training a YOLOv8 model on a custom dataset. In this mode
```python
from ultralytics import YOLO
model = YOLO('yolov8n.pt') # pass any model type
model = YOLO("yolov8n.pt") # pass any model type
results = model.train(epochs=5)
```
@ -65,8 +65,8 @@ Train mode is used for training a YOLOv8 model on a custom dataset. In this mode
```python
from ultralytics import YOLO
model = YOLO('yolov8n.yaml')
results = model.train(data='coco8.yaml', epochs=5)
model = YOLO("yolov8n.yaml")
results = model.train(data="coco8.yaml", epochs=5)
```
=== "Resume"
@ -117,14 +117,14 @@ Predict mode is used for making predictions using a trained YOLOv8 model on new
=== "From source"
```python
from ultralytics import YOLO
from PIL import Image
import cv2
from PIL import Image
from ultralytics import YOLO
model = YOLO("model.pt")
# accepts all formats - image/dir/Path/URL/video/PIL/ndarray. 0 for webcam
results = model.predict(source="0")
results = model.predict(source="folder", show=True) # Display preds. Accepts all YOLO predict arguments
results = model.predict(source="folder", show=True) # Display preds. Accepts all YOLO predict arguments
# from PIL
im1 = Image.open("bus.jpg")
@ -153,20 +153,20 @@ Predict mode is used for making predictions using a trained YOLOv8 model on new
for result in results:
# Detection
result.boxes.xyxy # box with xyxy format, (N, 4)
result.boxes.xywh # box with xywh format, (N, 4)
result.boxes.xyxy # box with xyxy format, (N, 4)
result.boxes.xywh # box with xywh format, (N, 4)
result.boxes.xyxyn # box with xyxy format but normalized, (N, 4)
result.boxes.xywhn # box with xywh format but normalized, (N, 4)
result.boxes.conf # confidence score, (N, 1)
result.boxes.cls # cls, (N, 1)
result.boxes.conf # confidence score, (N, 1)
result.boxes.cls # cls, (N, 1)
# Segmentation
result.masks.data # masks, (N, H, W)
result.masks.xy # x,y segments (pixels), List[segment] * N
result.masks.xyn # x,y segments (normalized), List[segment] * N
result.masks.data # masks, (N, H, W)
result.masks.xy # x,y segments (pixels), List[segment] * N
result.masks.xyn # x,y segments (normalized), List[segment] * N
# Classification
result.probs # cls prob, (num_class, )
result.probs # cls prob, (num_class, )
# Each result is composed of torch.Tensor by default,
# in which you can easily use following functionality:
@ -218,9 +218,9 @@ Track mode is used for tracking objects in real-time using a YOLOv8 model. In th
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n.pt') # load an official detection model
model = YOLO('yolov8n-seg.pt') # load an official segmentation model
model = YOLO('path/to/best.pt') # load a custom model
model = YOLO("yolov8n.pt") # load an official detection model
model = YOLO("yolov8n-seg.pt") # load an official segmentation model
model = YOLO("path/to/best.pt") # load a custom model
# Track with the model
results = model.track(source="https://youtu.be/LNwODJXcvt4", show=True)
@ -242,7 +242,7 @@ Benchmark mode is used to profile the speed and accuracy of various export forma
from ultralytics.utils.benchmarks import benchmark
# Benchmark
benchmark(model='yolov8n.pt', data='coco8.yaml', imgsz=640, half=False, device=0)
benchmark(model="yolov8n.pt", data="coco8.yaml", imgsz=640, half=False, device=0)
```
[Benchmark Examples](../modes/benchmark.md){ .md-button }
@ -259,18 +259,16 @@ Explorer API can be used to explore datasets with advanced semantic, vector-simi
from ultralytics import Explorer
# create an Explorer object
exp = Explorer(data='coco8.yaml', model='yolov8n.pt')
exp = Explorer(data="coco8.yaml", model="yolov8n.pt")
exp.create_embeddings_table()
similar = exp.get_similar(img='https://ultralytics.com/images/bus.jpg', limit=10)
similar = exp.get_similar(img="https://ultralytics.com/images/bus.jpg", limit=10)
print(similar.head())
# Search using multiple indices
similar = exp.get_similar(
img=['https://ultralytics.com/images/bus.jpg',
'https://ultralytics.com/images/bus.jpg'],
limit=10
)
img=["https://ultralytics.com/images/bus.jpg", "https://ultralytics.com/images/bus.jpg"], limit=10
)
print(similar.head())
```
@ -280,14 +278,14 @@ Explorer API can be used to explore datasets with advanced semantic, vector-simi
from ultralytics import Explorer
# create an Explorer object
exp = Explorer(data='coco8.yaml', model='yolov8n.pt')
exp = Explorer(data="coco8.yaml", model="yolov8n.pt")
exp.create_embeddings_table()
similar = exp.get_similar(idx=1, limit=10)
print(similar.head())
# Search using multiple indices
similar = exp.get_similar(idx=[1,10], limit=10)
similar = exp.get_similar(idx=[1, 10], limit=10)
print(similar.head())
```
@ -300,7 +298,7 @@ Explorer API can be used to explore datasets with advanced semantic, vector-simi
!!! Tip "Detection Trainer Example"
```python
from ultralytics.models.yolo import DetectionTrainer, DetectionValidator, DetectionPredictor
from ultralytics.models.yolo import DetectionPredictor, DetectionTrainer, DetectionValidator
# trainer
trainer = DetectionTrainer(overrides={})