Comments (12)
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.
If this is a bug report, please provide screenshots and minimum viable code to reproduce your issue, otherwise we can not help you.
If this is a custom model or data training question, please note that Ultralytics does not provide free personal support. As a leader in vision ML and AI, we do offer professional consulting, from simple expert advice up to delivery of fully customized, end-to-end production solutions for our clients, such as:
- Cloud-based AI surveillance systems operating on hundreds of HD video streams in realtime.
- Edge AI integrated into custom iOS and Android apps for realtime 30 FPS video inference.
- Custom data training, hyperparameter evolution, and model exportation to any destination.
For more information please visit https://www.ultralytics.com.
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@laizewei yes this should be possible. We use the yaml file to load multiple types of yolo models with the same pytorch code.
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Thank you!
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@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
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Can you offer Yolov5s.py that only use pytorch's base layer?I tried porting the libtorch version.
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Like this https://github.com/laizewei/libtorch-mobilenetv3/blob/master/MobileNet.h
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Hey
If I use Pytorch hub can I use my custom weights
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@sakshamjn no. The purpose of hub is to make official pretrained weights available in an easy to access method.
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@glenn-jocher Thanks.
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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.
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Hey
If I use Pytorch hub can I use my custom weights
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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! 😊
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Related Issues (20)
- Parameters Fusion HOT 1
- A question about bbox normalization HOT 2
- Unable to train model on VisDrone HOT 6
- Author, do you have a complete Python version that reads the engine model of Tensorrt to infer strength segmentation code, which is a simple version of the official inference code. It can be run in just one file without calling too many Python files or libraries HOT 1
- Android uses YOLOv5 segmentation HOT 3
- yolov5 Tensortt errors ? HOT 8
- about physical memory and virtual memory HOT 1
- _clip_augmented: clarifications required HOT 4
- After training my own dataset, the labels of pt model inference and engine model inference are inconsistent. HOT 3
- How to Show Real-Time Detection of Multiple Streams Using Titled Display Windows in Yolov5? HOT 4
- Class scores from TFlite model's output data don't add up to 1 HOT 4
- Model size is doubled when exporting model to onnx/torchscript HOT 2
- Labelling Objects Occluded objects in Extreme Environment HOT 4
- Trying to implement a custom dataset HOT 5
- Visualizing YOLOv5 Segmentation Data HOT 9
- no detection这个结果 HOT 7
- Save new video that only shows detections on filtered classes HOT 4
- Add support for MLProgram HOT 2
- Quantization and pruning to my pre-trained model HOT 5
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