Asserterror:'dict' object has no attribute load_state_dict

[SiYue1252]

Search before asking

• I have searched the YOLOv5 issues and discussions and found no similar questions.

Question

My code:
model = torch.load('yolov5_path/best.pt',map_location=device)
model.load_state_dict(torch.load('yolov5_path/state.pth',map_location=device))
model.eval()
Asserterror:'dict' object has no attribute load_state_dict
(state.pth came from :
import torch
m = torch.load("path/best.pt")
torch.save(m["model"].state_dict(), "path/state.pth")

Additional

No response

[github-actions]

👋 Hello @SiYue1252, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.

If this is a 🐛 Bug Report, please provide a minimum reproducible example to help us debug it.

If this is a custom training ❓ 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.

Requirements

Python>=3.8.0 with all requirements.txt installed including PyTorch>=1.8. To get started:

git clone https://github.com/ultralytics/yolov5  # clone
cd yolov5
pip install -r requirements.txt  # install

Environments

YOLOv5 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 YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training, validation, inference, export and benchmarks on macOS, Windows, and Ubuntu every 24 hours and on every commit.

Introducing YOLOv8 🚀

We're excited to announce the launch of our latest state-of-the-art (SOTA) object detection model for 2023 - YOLOv8 🚀!

Designed to be fast, accurate, and easy to use, YOLOv8 is an ideal choice for a wide range of object detection, image segmentation and image classification tasks. With YOLOv8, you'll be able to quickly and accurately detect objects in real-time, streamline your workflows, and achieve new levels of accuracy in your projects.

Check out our YOLOv8 Docs for details and get started with:

pip install ultralytics

[glenn-jocher]

Hello! 😊 It appears there's a small misunderstanding in how the model is loaded from the .pt file. torch.load('yolov5_path/best.pt') loads a pre-trained YOLOv5 model as a dictionary, including model weights and possibly other information, rather than directly as a model object. That's why you're seeing an error when trying to use .load_state_dict() on a dictionary. Here's a quick fix:

# Load the model
model = torch.load('yolov5_path/best.pt', map_location=device)['model']
# Now you can directly use the model for inference without loading state_dict again
model.eval()

This loads your model and sets it to evaluation mode, ready for inference. Remember, the torch.load('yolov5_path/best.pt') command returns the entire checkpoint, from which you only need the model for inference or further training tasks. No need to load state.pth again if you've already got everything in best.pt. Hope this clears things up!

[github-actions]

👋 Hello there! We wanted to give you a friendly reminder that this issue has not had any recent activity and may be closed soon, but don't worry - you can always reopen it if needed. If you still have any questions or concerns, please feel free to let us know how we can help.

For additional resources and information, please see the links below:

• Docs: https://docs.ultralytics.com
• HUB: https://hub.ultralytics.com
• Community: https://community.ultralytics.com

Feel free to inform us of any other issues you discover or feature requests that come to mind in the future. Pull Requests (PRs) are also always welcomed!

Thank you for your contributions to YOLO 🚀 and Vision AI ⭐