yoloworld offline use , set_classes adds specified weight path parameters about ultralytics HOT 2 CLOSED

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Install

Pip install the ultralytics package including all requirements in a Python>=3.8 environment with PyTorch>=1.8.

pip install ultralytics

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from ultralytics.

@reynaldliu hello! Thanks for reaching out with your request about modifying the default path for loading the clip model weights for offline usage.

You can achieve this by manually specifying the path when loading the model using clip.load(). You can pass an additional jit parameter pointing to your pre-downloaded weights file. Here's a modified snippet of your code:

import clip

def set_classes(self, text, clip_model_path):
    """Load custom CLIP model from a specific path."""
    model, _ = clip.load("ViT-B/32", jit=clip_model_path)
    device = next(model.parameters()).device
    text_token = clip.tokenize(text).to(device)

When calling set_classes, provide the local path to your pre-downloaded CLIP model file as the clip_model_path argument:

model.set_classes("Some classes text", clip_model_path="/local/path/to/your/clip/model.pt")

This should allow your application to run offline by leveraging the CLIP weights stored at a specified location. If you have any further questions or need additional assistance, please don't hesitate to ask! 🌐🚀

from ultralytics.