Can you provide a Yolov5 model that is not based on YAML files

[williamlzw]

Can you provide a Yolov5 model that is not based on YAML files?
I tried porting to libtorch.I can't understand the model based on yaml file.

[github-actions]

Hello @laizewei, thank you for your interest in our work! Please visit our Custom Training Tutorial to get started, and see our Google Colab Notebook, Docker Image, and GCP Quickstart Guide for example environments.

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[glenn-jocher]

@laizewei yes this should be possible. We use the yaml file to load multiple types of yolo models with the same pytorch code.

[williamlzw]

Thank you!

[glenn-jocher]

@laizewei you may be able to load YOLOv5 models now directly with PyTorch Hub. This way you should not have to deal with any yaml files. See https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading

[williamlzw]

Can you offer Yolov5s.py that only use pytorch's base layer?I tried porting the libtorch version.

[williamlzw]

Like this https://github.com/laizewei/libtorch-mobilenetv3/blob/master/MobileNet.h

[sakshamjn]

Hey
If I use Pytorch hub can I use my custom weights

[glenn-jocher]

@sakshamjn no. The purpose of hub is to make official pretrained weights available in an easy to access method.

[sakshamjn]

@glenn-jocher Thanks.

[github-actions]

This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.

[zhou-huan-1]

Hey
If I use Pytorch hub can I use my custom weights

[glenn-jocher]

Hey @zhou-huan-1! Yes, you can use your custom weights with PyTorch Hub by specifying the path parameter when loading the model. Here's a quick example:

import torch

# Load your custom model weights
model = torch.hub.load('ultralytics/yolov5', 'custom', path='path/to/your/custom_weights.pt')

This will allow you to use your trained weights for inference. Happy coding! 😊