batch-wise Predict works but not faster.

[mandal4]

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Question

I have question about Predict method in batchwise.
I see that model.predict(img_path_list) works but it dosen't faster with considerting total time cost.
What i want is to get same time with 1)model.predict(single_img) and 2)model.predict(multiple_img)
Could you tell me if i missed some point. Thx.

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[glenn-jocher]

Hello! Thanks for your question regarding batch-wise prediction speed using the YOLOv8 model. It sounds like you want to see similar time performance whether predicting on a single image or multiple images simultaneously.

If you're seeing slower predictions for batch mode relative to single image mode, this could be related to several factors, such as GPU utilization, I/O overhead from loading multiple images, or the efficient batching of GPU resources.

A few areas to explore:

• Ensure your GPU is optimally utilized: Check if your GPU is being fully utilized during batch predictions. Tools like nvidia-smi can help monitor this.
• Batch size adjustments: Consider adjusting the batch size for optimal performance based on your hardware specifications.
• Image preprocessing: Ensure that image loading and preprocessing steps are not causing a bottleneck.

Here's a simple usage example ensuring GPU is utilized if accessible:

from ultralytics import YOLO

# Load a model
model = YOLO('yolov8n.pt', device='cuda' if torch.cuda.is_available() else 'cpu')  # Ensuring it tries to use GPU if available

# Predict multiple images
results = model.predict(['im1.jpg', 'im2.jpg'])

If you continue to experience issues, profiling your inferencing times with detailed GPU metrics may offer more insights into where the delays occur. 🚀