Comments (2)
π Hello @BDhaese, thank you for your interest in Ultralytics YOLOv8 π! We recommend a visit to the Docs for new users where you can find many Python and CLI usage examples and where many of the most common questions may already be answered.
If this is a π Bug Report, please provide a minimum reproducible example to help us debug it.
If this is a custom training β Question, please provide as much information as possible, including dataset image examples and training logs, and verify you are following our Tips for Best Training Results.
Join the vibrant Ultralytics Discord π§ community for real-time conversations and collaborations. This platform offers a perfect space to inquire, showcase your work, and connect with fellow Ultralytics users.
Install
Pip install the ultralytics
package including all requirements in a Python>=3.8 environment with PyTorch>=1.8.
pip install ultralytics
Environments
YOLOv8 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):
- Notebooks with free GPU:
- Google Cloud Deep Learning VM. See GCP Quickstart Guide
- Amazon Deep Learning AMI. See AWS Quickstart Guide
- Docker Image. See Docker Quickstart Guide
Status
If this badge is green, all Ultralytics CI tests are currently passing. CI tests verify correct operation of all YOLOv8 Modes and Tasks on macOS, Windows, and Ubuntu every 24 hours and on every commit.
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@BDhaese hello! Thanks for reaching out with your issue regarding batch prediction with the NCNN model. π
It seems like the NCNN model might not support batch predictions in the same way the PyTorch model does. NCNN is generally optimized for single-image inference on mobile and embedded devices, which could explain the discrepancy you're experiencing.
As a workaround, you might consider running a loop to process each frame individually, like this:
import cv2
from ultralytics import YOLO
cap = cv2.VideoCapture(0)
model_ncnn = YOLO('yolov8s_ncnn_model')
model_ncnn.classes = [0]
results = []
while True:
ret, frame = cap.read()
if not ret:
break
result = model_ncnn.predict(source=frame)
results.append(result)
cap.release()
This approach processes each frame as it's captured, which should avoid the batch processing issue.
Let us know if this helps or if you have any more questions! π
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Related Issues (20)
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- Postprocess Yolov8-segmentation raw prediction HOT 4
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