.pth

[2029686068]

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TypeError: model='epoch_20SSD.pth' should be a *.pt PyTorch model to run this method, but is a different format. PyTorch models can train, val, predict and export, i.e. 'model.train(data=...)', but exported formats like ONNX, TensorRT etc. only support 'predict' and 'val' modes, i.e. 'yolo predict model=yolov8n.onnx'. To run CUDA or MPS inference please pass the device argument directly in your inference command, i.e. 'model.predict(source=..., device=0)',我该怎么转换成.pth

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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

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.

[glenn-jocher]

Hello! It seems like you're trying to use a .pth file with YOLOv8, which expects a .pt file format for its model files. If you have a PyTorch checkpoint in .pth format, you'll first need to load it properly and save it in .pt format, suitable for using with YOLOv8. Here's a quick example on how to do that:

import torch
from ultralytics import YOLO

# Load your .pth file
model_state = torch.load('epoch_20SSD.pth')

# You may need to initialize your model architecture here if necessary
# For example:
# model = SomeModelClass(*args, **kwargs)
# model.load_state_dict(model_state)

# Now, create a YOLO model instance
# You might need the correct model config file or use a predefined model
model = YOLO('yolov8n.yaml')

# Load the state dict into the YOLO model
model.model.load_state_dict(model_state)
# or if the state dict was saved with the Ultralytics format:
# model.load_state_dict(model_state['model'])

# Finally, save it in .pt format
torch.save(model.model.state_dict(), 'epoch_20SSD.pt')

This converts your .pth file to the .pt format expected by YOLOv8. Make sure to customize the model architecture loading part as per your specific model setup. Let me know if there's anything else you need help with! 😊