@ -8,6 +8,17 @@ keywords: YOLOv8, PaddlePaddle, export models, computer vision, deep learning, m
Bridging the gap between developing and deploying computer vision models in real-world scenarios with varying conditions can be difficult. PaddlePaddle makes this process easier with its focus on flexibility, performance, and its capability for parallel processing in distributed environments. This means you can use your YOLOv8 computer vision models on a wide variety of devices and platforms, from smartphones to cloud-based servers.
<strong>Watch:</strong> How to Export Ultralytics YOLOv8 Models to PaddlePaddle Format | Key Features of PaddlePaddle Format
</p>
The ability to export to PaddlePaddle model format allows you to optimize your [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) models for use within the PaddlePaddle framework. PaddlePaddle is known for facilitating industrial deployments and is a good choice for deploying computer vision applications in real-world settings across various domains.
@ -328,9 +328,10 @@ Below are code examples for using each source type:
results = model(source, stream=True) # generator of Results objects
```
=== "Streams"
=== "Stream"
Use the stream mode to run inference on live video streams using RTSP, RTMP, TCP, or IP address protocols. If a single stream is provided, the model runs inference with a batch size of 1. For multiple streams, a `.streams` text file can be used to perform batched inference, where the batch size is determined by the number of streams provided (e.g., batch-size 8 for 8 streams).
Run inference on remote streaming sources using RTSP, RTMP, TCP and IP address protocols. If multiple streams are provided in a `*.streams` text file then batched inference will run, i.e. 8 streams will run at batch-size 8, otherwise single streams will run at batch-size 1.
```python
from ultralytics import YOLO
@ -338,15 +339,43 @@ Below are code examples for using each source type:
model = YOLO("yolov8n.pt")
# Single stream with batch-size 1 inference
source = "rtsp://example.com/media.mp4" # RTSP, RTMP, TCP or IP streaming address
source = "rtsp://example.com/media.mp4" # RTSP, RTMP, TCP, or IP streaming address
# Run inference on the source
results = model(source, stream=True) # generator of Results objects
```
For single stream usage, the batch size is set to 1 by default, allowing efficient real-time processing of the video feed.
=== "Multi-Stream"
To handle multiple video streams simultaneously, use a `.streams` text file containing the streaming sources. The model will run batched inference where the batch size equals the number of streams. This setup enables efficient processing of multiple feeds concurrently.
```python
from ultralytics import YOLO
# Load a pretrained YOLOv8n model
model = YOLO("yolov8n.pt")
# Multiple streams with batched inference (i.e. batch-size 8 for 8 streams)
source = "path/to/list.streams" # *.streams text file with one streaming address per row
# Multiple streams with batched inference (e.g., batch-size 8 for 8 streams)
source = "path/to/list.streams" # *.streams text file with one streaming address per line
# Run inference on the source
results = model(source, stream=True) # generator of Results objects
```
Example `.streams` text file:
```txt
rtsp://example.com/media1.mp4
rtsp://example.com/media2.mp4
rtmp://example2.com/live
tcp://192.168.1.100:554
...
```
Each row in the file represents a streaming source, allowing you to monitor and perform inference on several video streams at once.
## Inference Arguments
`model.predict()` accepts multiple arguments that can be passed at inference time to override defaults:
@ -19,28 +19,17 @@ The output of an oriented object detector is a set of rotated bounding boxes tha
YOLOv8 OBB models use the `-obb` suffix, i.e. `yolov8n-obb.pt` and are pretrained on [DOTAv1](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/DOTAv1.yaml).
Francesco Mattioli
5 months ago
committed by
GitHub