Comments (6)
@smallMantou model.predict("path/to/imgs")
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Do I need to list each image for detecting multiple images?
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Do I need to list each image for detecting multiple images?
no just give the path to the img directory
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Thank you for your answer.How do I know which model I am using when training my dataset, because I want to start training from scratch, but the yolo. yaml file contains n \ s \ m \ l.Grayscale_yolov5s.yaml is yolov8.yaml that I copied.
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Hello! To determine which model configuration you are using when training from scratch, you should look at the YAML file you are specifying in your training command. Each model variant (n, s, m, l) has its own YAML file that defines the model's architecture and settings. If you've copied and renamed a YAML file, make sure to check its contents to see which model it's configured for.
For example, if you're using Grayscale_yolov5s.yaml
and you've renamed it from a YOLOv8 model configuration, you should open that YAML file and review the parameters to confirm which model size it corresponds to (n, s, m, l). The model size is typically indicated by the depth and width parameters in the YAML file.
Here's a quick way to start training from scratch using a specific model configuration:
yolo train data=your_dataset.yaml model=your_model.yaml
Replace your_dataset.yaml
with your dataset configuration file and your_model.yaml
with the model configuration file you intend to use. This will ensure you are training with the correct model setup. Happy training! π
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π 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 π and Vision AI β
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