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Docs Ruff codeblocks reformat and fix (#12847)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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6
docs/en/datasets/pose/tiger-pose.md
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@ -80,17 +80,17 @@ The example showcases the variety and complexity of the images in the Tiger-Pose
from ultralytics import YOLO
# Load a model
model = YOLO('path/to/best.pt') # load a tiger-pose trained model
model = YOLO("path/to/best.pt") # load a tiger-pose trained model
# Run inference
results = model.predict(source="https://www.youtube.com/watch?v=MIBAT6BGE6U&pp=ygUYdGlnZXIgd2Fsa2luZyByZWZlcmVuY2Ug" show=True)
results = model.predict(source="https://youtu.be/MIBAT6BGE6U", show=True)
```
=== "CLI"
```bash
# Run inference using a tiger-pose trained model
yolo task=pose mode=predict source="https://www.youtube.com/watch?v=MIBAT6BGE6U&pp=ygUYdGlnZXIgd2Fsa2luZyByZWZlcmVuY2Ug" show=True model="path/to/best.pt"
yolo task=pose mode=predict source="https://youtu.be/MIBAT6BGE6U" show=True model="path/to/best.pt"
```
## Citations and Acknowledgments

2
docs/en/guides/index.md
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@ -35,7 +35,7 @@ Here's a compilation of in-depth guides to help you master different aspects of
- [Conda Quickstart](conda-quickstart.md) 🚀 NEW: Step-by-step guide to setting up a [Conda](https://anaconda.org/conda-forge/ultralytics) environment for Ultralytics. Learn how to install and start using the Ultralytics package efficiently with Conda.
- [Docker Quickstart](docker-quickstart.md) 🚀 NEW: Complete guide to setting up and using Ultralytics YOLO models with [Docker](https://hub.docker.com/r/ultralytics/ultralytics). Learn how to install Docker, manage GPU support, and run YOLO models in isolated containers for consistent development and deployment.
- [Raspberry Pi](raspberry-pi.md) 🚀 NEW: Quickstart tutorial to run YOLO models to the latest Raspberry Pi hardware.
- [Nvidia-Jetson](nvidia-jetson.md)🚀 NEW: Quickstart guide for deploying YOLO models on Nvidia Jetson devices.
- [NVIDIA-Jetson](nvidia-jetson.md)🚀 NEW: Quickstart guide for deploying YOLO models on NVIDIA Jetson devices.
- [Triton Inference Server Integration](triton-inference-server.md) 🚀 NEW: Dive into the integration of Ultralytics YOLOv8 with NVIDIA's Triton Inference Server for scalable and efficient deep learning inference deployments.
- [YOLO Thread-Safe Inference](yolo-thread-safe-inference.md) 🚀 NEW: Guidelines for performing inference with YOLO models in a thread-safe manner. Learn the importance of thread safety and best practices to prevent race conditions and ensure consistent predictions.
- [Isolating Segmentation Objects](isolating-segmentation-objects.md) 🚀 NEW: Step-by-step recipe and explanation on how to extract and/or isolate objects from images using Ultralytics Segmentation.

23
docs/en/guides/isolating-segmentation-objects.md
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@ -63,13 +63,12 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
# (2) Iterate detection results (helpful for multiple images)
for r in res:
img = np.copy(r.orig_img)
img_name = Path(r.path).stem # source image base-name
img_name = Path(r.path).stem # source image base-name
# Iterate each object contour (multiple detections)
for ci,c in enumerate(r):
for ci, c in enumerate(r):
# (1) Get detection class name
label = c.names[c.boxes.cls.tolist().pop()]
```
1. To learn more about working with detection results, see [Boxes Section for Predict Mode](../modes/predict.md#boxes).
@ -98,12 +97,7 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
# Draw contour onto mask
_ = cv2.drawContours(b_mask,
[contour],
-1,
(255, 255, 255),
cv2.FILLED)
_ = cv2.drawContours(b_mask, [contour], -1, (255, 255, 255), cv2.FILLED)
```
1. For more info on `c.masks.xy` see [Masks Section from Predict Mode](../modes/predict.md#masks).
@ -280,16 +274,16 @@ import cv2
import numpy as np
from ultralytics import YOLO
m = YOLO('yolov8n-seg.pt')#(4)!
res = m.predict()#(3)!
m = YOLO("yolov8n-seg.pt") # (4)!
res = m.predict() # (3)!
# iterate detection results (5)
# Iterate detection results (5)
for r in res:
img = np.copy(r.orig_img)
img_name = Path(r.path).stem
# iterate each object contour (6)
for ci,c in enumerate(r):
# Iterate each object contour (6)
for ci, c in enumerate(r):
label = c.names[c.boxes.cls.tolist().pop()]
b_mask = np.zeros(img.shape[:2], np.uint8)
@ -312,7 +306,6 @@ for r in res:
iso_crop = isolated[y1:y2, x1:x2]
# TODO your actions go here (2)
```
1. The line populating `contour` is combined into a single line here, where it was split to multiple above.

17
docs/en/guides/view-results-in-terminal.md
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@ -61,9 +61,9 @@ The VSCode compatible protocols for viewing images using the integrated terminal
# Run inference on an image
results = model.predict(source="ultralytics/assets/bus.jpg")
# Plot inference results
plot = results[0].plot() #(1)!
plot = results[0].plot() # (1)!
```
1. See [plot method parameters](../modes/predict.md#plot-method-parameters) to see possible arguments to use.
@ -73,9 +73,9 @@ The VSCode compatible protocols for viewing images using the integrated terminal
```{ .py .annotate }
# Results image as bytes
im_bytes = cv.imencode(
".png", #(1)!
".png", # (1)!
plot,
)[1].tobytes() #(2)!
)[1].tobytes() # (2)!
# Image bytes as a file-like object
mem_file = io.BytesIO(im_bytes)
@ -110,9 +110,8 @@ The VSCode compatible protocols for viewing images using the integrated terminal
import io
import cv2 as cv
from ultralytics import YOLO
from sixel import SixelWriter
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.pt")
@ -121,13 +120,13 @@ model = YOLO("yolov8n.pt")
results = model.predict(source="ultralytics/assets/bus.jpg")
# Plot inference results
plot = results[0].plot() #(3)!
plot = results[0].plot() # (3)!
# Results image as bytes
im_bytes = cv.imencode(
".png", #(1)!
".png", # (1)!
plot,
)[1].tobytes() #(2)!
)[1].tobytes() # (2)!
mem_file = io.BytesIO(im_bytes)
w = SixelWriter()

6
docs/en/integrations/tensorboard.md
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@ -39,15 +39,15 @@ TensorBoard is conveniently pre-installed with YOLOv8, eliminating the need for
For detailed instructions and best practices related to the installation process, be sure to check our [YOLOv8 Installation guide](../quickstart.md). While installing the required packages for YOLOv8, if you encounter any difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips.
## Configuring TensorBoard for Google Collab
## Configuring TensorBoard for Google Colab
When using Google Colab, it's important to set up TensorBoard before starting your training code:
!!! Example "Configure TensorBoard for Google Collab"
!!! Example "Configure TensorBoard for Google Colab"
=== "Python"
```python
```ipython
%load_ext tensorboard
%tensorboard --logdir path/to/runs
```

43
docs/en/integrations/tensorrt.md
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@ -153,15 +153,16 @@ Experimentation by NVIDIA led them to recommend using at least 500 calibration i
model = YOLO("yolov8n.pt")
model.export(
format="engine",
dynamic=True, #(1)!
batch=8, #(2)!
workspace=4, #(3)!
dynamic=True, # (1)!
batch=8, # (2)!
workspace=4, # (3)!
int8=True,
data="coco.yaml", #(4)!
data="coco.yaml", # (4)!
)
# Load the exported TensorRT INT8 model
model = YOLO("yolov8n.engine", task="detect")
# Run inference
result = model.predict("https://ultralytics.com/images/bus.jpg")
```
@ -385,36 +386,14 @@ Expand sections below for information on how these models were exported and test
model = YOLO("yolov8n.pt")
# TensorRT FP32
out = model.export(
format="engine",
imgsz:640,
dynamic:True,
verbose:False,
batch:8,
workspace:2
)
out = model.export(format="engine", imgsz=640, dynamic=True, verbose=False, batch=8, workspace=2)
# TensorRT FP16
out = model.export(format="engine", imgsz=640, dynamic=True, verbose=False, batch=8, workspace=2, half=True)
# TensorRT INT8 with calibration `data` (i.e. COCO, ImageNet, or DOTAv1 for appropriate model task)
out = model.export(
format="engine",
imgsz:640,
dynamic:True,
verbose:False,
batch:8,
workspace:2,
half=True
)
# TensorRT INT8
out = model.export(
format="engine",
imgsz:640,
dynamic:True,
verbose:False,
batch:8,
workspace:2,
int8=True,
data:"data.yaml" # COCO, ImageNet, or DOTAv1 for appropriate model task
format="engine", imgsz=640, dynamic=True, verbose=False, batch=8, workspace=2, int8=True, data="coco8.yaml"
)
```

42
docs/en/usage/python.md
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@ -87,23 +87,31 @@ Val mode is used for validating a YOLOv8 model after it has been trained. In thi
=== "Val after training"
```python
from ultralytics import YOLO
from ultralytics import YOLO
model = YOLO('yolov8n.yaml')
model.train(data='coco8.yaml', epochs=5)
model.val() # It'll automatically evaluate the data you trained.
# Load a YOLOv8 model
model = YOLO("yolov8n.yaml")
# Train the model
model.train(data="coco8.yaml", epochs=5)
# Validate on training data
model.val()
```
=== "Val independently"
=== "Val on another dataset"
```python
from ultralytics import YOLO
from ultralytics import YOLO
model = YOLO("model.pt")
# It'll use the data YAML file in model.pt if you don't set data.
model.val()
# or you can set the data you want to val
model.val(data='coco8.yaml')
# Load a YOLOv8 model
model = YOLO("yolov8n.yaml")
# Train the model
model.train(data="coco8.yaml", epochs=5)
# Validate on separate data
model.val(data="path/to/separate/data.yaml")
```
[Val Examples](../modes/val.md){ .md-button }
@ -188,20 +196,20 @@ Export mode is used for exporting a YOLOv8 model to a format that can be used fo
Export an official YOLOv8n model to ONNX with dynamic batch-size and image-size.
```python
from ultralytics import YOLO
from ultralytics import YOLO
model = YOLO('yolov8n.pt')
model.export(format='onnx', dynamic=True)
model = YOLO("yolov8n.pt")
model.export(format="onnx", dynamic=True)
```
=== "Export to TensorRT"
Export an official YOLOv8n model to TensorRT on `device=0` for acceleration on CUDA devices.
```python
from ultralytics import YOLO
from ultralytics import YOLO
model = YOLO('yolov8n.pt')
model.export(format='onnx', device=0)
model = YOLO("yolov8n.pt")
model.export(format="onnx", device=0)
```
[Export Examples](../modes/export.md){ .md-button }

202
docs/en/usage/simple-utilities.md
View file

@ -36,10 +36,10 @@ Dataset annotation is a very resource intensive and time-consuming process. If y
```{ .py .annotate }
from ultralytics.data.annotator import auto_annotate
auto_annotate(#(1)!
data='path/to/new/data',
det_model='yolov8n.pt',
sam_model='mobile_sam.pt',
auto_annotate( # (1)!
data="path/to/new/data",
det_model="yolov8n.pt",
sam_model="mobile_sam.pt",
device="cuda",
output_dir="path/to/save_labels",
)
@ -58,9 +58,9 @@ Use to convert COCO JSON annotations into proper YOLO format. For object detecti
```{ .py .annotate }
from ultralytics.data.converter import convert_coco
convert_coco(#(1)!
'../datasets/coco/annotations/',
use_segments=False,
convert_coco( # (1)!
"../datasets/coco/annotations/",
use_segments=False,
use_keypoints=False,
cls91to80=True,
)
@ -113,10 +113,10 @@ data
```{ .py .annotate }
from ultralytics.data.converter import yolo_bbox2segment
yolo_bbox2segment(#(1)!
yolo_bbox2segment( # (1)!
im_dir="path/to/images",
save_dir=None, # saved to "labels-segment" in images directory
sam_model="sam_b.pt"
save_dir=None, # saved to "labels-segment" in images directory
sam_model="sam_b.pt",
)
```
@ -129,20 +129,22 @@ yolo_bbox2segment(#(1)!
If you have a dataset that uses the [segmentation dataset format](../datasets/segment/index.md) you can easily convert these into up-right (or horizontal) bounding boxes (`x y w h` format) with this function.
```python
import numpy as np
from ultralytics.utils.ops import segments2boxes
segments = np.array(
[[805, 392, 797, 400, ..., 808, 714, 808, 392],
[115, 398, 113, 400, ..., 150, 400, 149, 298],
[267, 412, 265, 413, ..., 300, 413, 299, 412],
[
[805, 392, 797, 400, ..., 808, 714, 808, 392],
[115, 398, 113, 400, ..., 150, 400, 149, 298],
[267, 412, 265, 413, ..., 300, 413, 299, 412],
]
)
segments2boxes([s.reshape(-1,2) for s in segments])
>>> array([[ 741.66, 631.12, 133.31, 479.25],
[ 146.81, 649.69, 185.62, 502.88],
[ 281.81, 636.19, 118.12, 448.88]],
dtype=float32) # xywh bounding boxes
segments2boxes([s.reshape(-1, 2) for s in segments])
# >>> array([[ 741.66, 631.12, 133.31, 479.25],
# [ 146.81, 649.69, 185.62, 502.88],
# [ 281.81, 636.19, 118.12, 448.88]],
# dtype=float32) # xywh bounding boxes
```
To understand how this function works, visit the [reference page](../reference/utils/ops.md#ultralytics.utils.ops.segments2boxes)
@ -155,10 +157,11 @@ Compresses a single image file to reduced size while preserving its aspect ratio
```{ .py .annotate }
from pathlib import Path
from ultralytics.data.utils import compress_one_image
for f in Path('path/to/dataset').rglob('*.jpg'):
compress_one_image(f)#(1)!
for f in Path("path/to/dataset").rglob("*.jpg"):
compress_one_image(f) # (1)!
```
1. Nothing returns from this function
@ -170,10 +173,10 @@ Automatically split a dataset into `train`/`val`/`test` splits and save the resu
```{ .py .annotate }
from ultralytics.data.utils import autosplit
autosplit( #(1)!
autosplit( # (1)!
path="path/to/images",
weights=(0.9, 0.1, 0.0), # (train, validation, test) fractional splits
annotated_only=False # split only images with annotation file when True
weights=(0.9, 0.1, 0.0), # (train, validation, test) fractional splits
annotated_only=False, # split only images with annotation file when True
)
```
@ -194,9 +197,7 @@ import numpy as np
from ultralytics.data.utils import polygon2mask
imgsz = (1080, 810)
polygon = np.array(
[805, 392, 797, 400, ..., 808, 714, 808, 392], # (238, 2)
)
polygon = np.array([805, 392, 797, 400, ..., 808, 714, 808, 392]) # (238, 2)
mask = polygon2mask(
imgsz, # tuple
@ -213,32 +214,36 @@ mask = polygon2mask(
To manage bounding box data, the `Bboxes` class will help to convert between box coordinate formatting, scale box dimensions, calculate areas, include offsets, and more!
```python
import numpy as np
from ultralytics.utils.instance import Bboxes
boxes = Bboxes(
bboxes=np.array(
[[ 22.878, 231.27, 804.98, 756.83,],
[ 48.552, 398.56, 245.35, 902.71,],
[ 669.47, 392.19, 809.72, 877.04,],
[ 221.52, 405.8, 344.98, 857.54,],
[ 0, 550.53, 63.01, 873.44,],
[ 0.0584, 254.46, 32.561, 324.87,]]
[
[22.878, 231.27, 804.98, 756.83],
[48.552, 398.56, 245.35, 902.71],
[669.47, 392.19, 809.72, 877.04],
[221.52, 405.8, 344.98, 857.54],
[0, 550.53, 63.01, 873.44],
[0.0584, 254.46, 32.561, 324.87],
]
),
format="xyxy",
)
boxes.areas()
>>> array([ 4.1104e+05, 99216, 68000, 55772, 20347, 2288.5])
# >>> array([ 4.1104e+05, 99216, 68000, 55772, 20347, 2288.5])
boxes.convert("xywh")
boxes.bboxes
>>> array(
[[ 413.93, 494.05, 782.1, 525.56],
[ 146.95, 650.63, 196.8, 504.15],
[ 739.6, 634.62, 140.25, 484.85],
[ 283.25, 631.67, 123.46, 451.74],
[ 31.505, 711.99, 63.01, 322.91],
[ 16.31, 289.67, 32.503, 70.41]]
)
print(boxes.bboxes)
# >>> array(
# [[ 413.93, 494.05, 782.1, 525.56],
# [ 146.95, 650.63, 196.8, 504.15],
# [ 739.6, 634.62, 140.25, 484.85],
# [ 283.25, 631.67, 123.46, 451.74],
# [ 31.505, 711.99, 63.01, 322.91],
# [ 16.31, 289.67, 32.503, 70.41]]
# )
```
See the [`Bboxes` reference section](../reference/utils/instance.md#ultralytics.utils.instance.Bboxes) for more attributes and methods available.
@ -257,37 +262,39 @@ import numpy as np
from ultralytics.utils.ops import scale_boxes
image = cv.imread("ultralytics/assets/bus.jpg")
*(h, w), c = image.shape
h, w, c = image.shape
resized = cv.resize(image, None, (), fx=1.2, fy=1.2)
*(new_h, new_w), _ = resized.shape
new_h, new_w, _ = resized.shape
xyxy_boxes = np.array(
[[ 22.878, 231.27, 804.98, 756.83,],
[ 48.552, 398.56, 245.35, 902.71,],
[ 669.47, 392.19, 809.72, 877.04,],
[ 221.52, 405.8, 344.98, 857.54,],
[ 0, 550.53, 63.01, 873.44,],
[ 0.0584, 254.46, 32.561, 324.87,]]
[
[22.878, 231.27, 804.98, 756.83],
[48.552, 398.56, 245.35, 902.71],
[669.47, 392.19, 809.72, 877.04],
[221.52, 405.8, 344.98, 857.54],
[0, 550.53, 63.01, 873.44],
[0.0584, 254.46, 32.561, 324.87],
]
)
new_boxes = scale_boxes(
img1_shape=(h, w), # original image dimensions
boxes=xyxy_boxes, # boxes from original image
img1_shape=(h, w), # original image dimensions
boxes=xyxy_boxes, # boxes from original image
img0_shape=(new_h, new_w), # resized image dimensions (scale to)
ratio_pad=None,
padding=False,
xywh=False,
)
new_boxes#(1)!
>>> array(
[[ 27.454, 277.52, 965.98, 908.2],
[ 58.262, 478.27, 294.42, 1083.3],
[ 803.36, 470.63, 971.66, 1052.4],
[ 265.82, 486.96, 413.98, 1029],
[ 0, 660.64, 75.612, 1048.1],
[ 0.0701, 305.35, 39.073, 389.84]]
)
print(new_boxes) # (1)!
# >>> array(
# [[ 27.454, 277.52, 965.98, 908.2],
# [ 58.262, 478.27, 294.42, 1083.3],
# [ 803.36, 470.63, 971.66, 1052.4],
# [ 265.82, 486.96, 413.98, 1029],
# [ 0, 660.64, 75.612, 1048.1],
# [ 0.0701, 305.35, 39.073, 389.84]]
# )
```
1. Bounding boxes scaled for the new image size
@ -303,24 +310,26 @@ import numpy as np
from ultralytics.utils.ops import xyxy2xywh
xyxy_boxes = np.array(
[[ 22.878, 231.27, 804.98, 756.83,],
[ 48.552, 398.56, 245.35, 902.71,],
[ 669.47, 392.19, 809.72, 877.04,],
[ 221.52, 405.8, 344.98, 857.54,],
[ 0, 550.53, 63.01, 873.44,],
[ 0.0584, 254.46, 32.561, 324.87,]]
[
[22.878, 231.27, 804.98, 756.83],
[48.552, 398.56, 245.35, 902.71],
[669.47, 392.19, 809.72, 877.04],
[221.52, 405.8, 344.98, 857.54],
[0, 550.53, 63.01, 873.44],
[0.0584, 254.46, 32.561, 324.87],
]
)
xywh = xyxy2xywh(xyxy_boxes)
xywh
>>> array(
[[ 413.93, 494.05, 782.1, 525.56],
[ 146.95, 650.63, 196.8, 504.15],
[ 739.6, 634.62, 140.25, 484.85],
[ 283.25, 631.67, 123.46, 451.74],
[ 31.505, 711.99, 63.01, 322.91],
[ 16.31, 289.67, 32.503, 70.41]]
)
print(xywh)
# >>> array(
# [[ 413.93, 494.05, 782.1, 525.56],
# [ 146.95, 650.63, 196.8, 504.15],
# [ 739.6, 634.62, 140.25, 484.85],
# [ 283.25, 631.67, 123.46, 451.74],
# [ 31.505, 711.99, 63.01, 322.91],
# [ 16.31, 289.67, 32.503, 70.41]]
# )
```
### All Bounding Box Conversions
@ -352,9 +361,9 @@ import cv2 as cv
import numpy as np
from ultralytics.utils.plotting import Annotator, colors
names { #(1)!
0: "person",
5: "bus",
names = { # (1)!
0: "person",
5: "bus",
11: "stop sign",
}
@ -362,18 +371,20 @@ image = cv.imread("ultralytics/assets/bus.jpg")
ann = Annotator(
image,
line_width=None, # default auto-size
font_size=None, # default auto-size
font="Arial.ttf", # must be ImageFont compatible
pil=False, # use PIL, otherwise uses OpenCV
font_size=None, # default auto-size
font="Arial.ttf", # must be ImageFont compatible
pil=False, # use PIL, otherwise uses OpenCV
)
xyxy_boxes = np.array(
[[ 5, 22.878, 231.27, 804.98, 756.83,], # class-idx x1 y1 x2 y2
[ 0, 48.552, 398.56, 245.35, 902.71,],
[ 0, 669.47, 392.19, 809.72, 877.04,],
[ 0, 221.52, 405.8, 344.98, 857.54,],
[ 0, 0, 550.53, 63.01, 873.44,],
[11, 0.0584, 254.46, 32.561, 324.87,]]
[
[5, 22.878, 231.27, 804.98, 756.83], # class-idx x1 y1 x2 y2
[0, 48.552, 398.56, 245.35, 902.71],
[0, 669.47, 392.19, 809.72, 877.04],
[0, 221.52, 405.8, 344.98, 857.54],
[0, 0, 550.53, 63.01, 873.44],
[11, 0.0584, 254.46, 32.561, 324.87],
]
)
for nb, box in enumerate(xyxy_boxes):
@ -412,7 +423,7 @@ ann = Annotator(
for obb in obb_boxes:
c_idx, *obb = obb
obb = np.array(obb).reshape(-1, 4, 2).squeeze()
label = f"{names.get(int(c_idx))}"
label = f"{obb_names.get(int(c_idx))}"
ann.box_label(
obb,
label,
@ -434,11 +445,11 @@ Check duration for code to run/process either using `with` or as a decorator.
```python
from ultralytics.utils.ops import Profile
with Profile(device=device) as dt:
with Profile(device="cuda:0") as dt:
pass # operation to measure
print(dt)
>>> "Elapsed time is 9.5367431640625e-07 s"
# >>> "Elapsed time is 9.5367431640625e-07 s"
```
### Ultralytics Supported Formats
@ -446,11 +457,10 @@ print(dt)
Want or need to use the formats of [images or videos types supported](../modes/predict.md#image-and-video-formats) by Ultralytics programmatically? Use these constants if you need.
```python
from ultralytics.data.utils import IMG_FORMATS
from ultralytics.data.utils import VID_FORMATS
from ultralytics.data.utils import IMG_FORMATS, VID_FORMATS
print(IMG_FORMATS)
>>> ('bmp', 'dng', 'jpeg', 'jpg', 'mpo', 'png', 'tif', 'tiff', 'webp', 'pfm')
# >>> ('bmp', 'dng', 'jpeg', 'jpg', 'mpo', 'png', 'tif', 'tiff', 'webp', 'pfm')
```
### Make Divisible
@ -461,7 +471,7 @@ Calculates the nearest whole number to `x` to make evenly divisible when divided
from ultralytics.utils.ops import make_divisible
make_divisible(7, 3)
>>> 9
# >>> 9
make_divisible(7, 2)
>>> 8
# >>> 8
```

4
docs/en/yolov5/environments/docker_image_quickstart_tutorial.md
View file

@ -12,8 +12,8 @@ You can also explore other quickstart options for YOLOv5, such as our [Colab Not
## Prerequisites
1. **Nvidia Driver**: Version 455.23 or higher. Download from [Nvidia's website](https://www.nvidia.com/Download/index.aspx).
2. **Nvidia-Docker**: Allows Docker to interact with your local GPU. Installation instructions are available on the [Nvidia-Docker GitHub repository](https://github.com/NVIDIA/nvidia-docker).
1. **NVIDIA Driver**: Version 455.23 or higher. Download from [Nvidia's website](https://www.nvidia.com/Download/index.aspx).
2. **NVIDIA-Docker**: Allows Docker to interact with your local GPU. Installation instructions are available on the [NVIDIA-Docker GitHub repository](https://github.com/NVIDIA/nvidia-docker).
3. **Docker Engine - CE**: Version 19.03 or higher. Download and installation instructions can be found on the [Docker website](https://docs.docker.com/install/).
## Step 1: Pull the YOLOv5 Docker Image