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Threaded inference docs improvements (#16313)

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Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Glenn Jocher 2024-09-16 20:47:37 +02:00 • committed by GitHub
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18
docs/en/modes/predict.md
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@ -61,7 +61,7 @@ Ultralytics YOLO models return either a Python list of `Results` objects, or a m
model = YOLO("yolov8n.pt") # pretrained YOLOv8n model
# Run batched inference on a list of images
results = model(["im1.jpg", "im2.jpg"]) # return a list of Results objects
results = model(["image1.jpg", "image2.jpg"]) # return a list of Results objects
# Process results list
for result in results:
@ -83,7 +83,7 @@ Ultralytics YOLO models return either a Python list of `Results` objects, or a m
model = YOLO("yolov8n.pt") # pretrained YOLOv8n model
# Run batched inference on a list of images
results = model(["im1.jpg", "im2.jpg"], stream=True) # return a generator of Results objects
results = model(["image1.jpg", "image2.jpg"], stream=True) # return a generator of Results objects
# Process results generator
for result in results:
@ -109,8 +109,8 @@ YOLOv8 can process different types of input sources for inference, as shown in t
| image | `'image.jpg'` | `str` or `Path` | Single image file. |
| URL | `'https://ultralytics.com/images/bus.jpg'` | `str` | URL to an image. |
| screenshot | `'screen'` | `str` | Capture a screenshot. |
| PIL | `Image.open('im.jpg')` | `PIL.Image` | HWC format with RGB channels. |
| OpenCV | `cv2.imread('im.jpg')` | `np.ndarray` | HWC format with BGR channels `uint8 (0-255)`. |
| PIL | `Image.open('image.jpg')` | `PIL.Image` | HWC format with RGB channels. |
| OpenCV | `cv2.imread('image.jpg')` | `np.ndarray` | HWC format with BGR channels `uint8 (0-255)`. |
| numpy | `np.zeros((640,1280,3))` | `np.ndarray` | HWC format with BGR channels `uint8 (0-255)`. |
| torch | `torch.zeros(16,3,320,640)` | `torch.Tensor` | BCHW format with RGB channels `float32 (0.0-1.0)`. |
| CSV | `'sources.csv'` | `str` or `Path` | CSV file containing paths to images, videos, or directories. |
@ -710,16 +710,16 @@ When using YOLO models in a multi-threaded application, it's important to instan
from ultralytics import YOLO
def thread_safe_predict(image_path):
def thread_safe_predict(model, image_path):
"""Performs thread-safe prediction on an image using a locally instantiated YOLO model."""
local_model = YOLO("yolov8n.pt")
results = local_model.predict(image_path)
model = YOLO(model)
results = model.predict(image_path)
# Process results
# Starting threads that each have their own model instance
Thread(target=thread_safe_predict, args=("image1.jpg",)).start()
Thread(target=thread_safe_predict, args=("image2.jpg",)).start()
Thread(target=thread_safe_predict, args=("yolov8n.pt", "image1.jpg")).start()
Thread(target=thread_safe_predict, args=("yolov8n.pt", "image2.jpg")).start()
```
For an in-depth look at thread-safe inference with YOLO models and step-by-step instructions, please refer to our [YOLO Thread-Safe Inference Guide](../guides/yolo-thread-safe-inference.md). This guide will provide you with all the necessary information to avoid common pitfalls and ensure that your multi-threaded inference runs smoothly.