Comments (2)
@moahaimen π Hello! Thanks for asking about YOLOv5 π architecture visualization. We've made visualizing YOLOv5 π architectures super easy. There are 3 main ways:
model.yaml
Each model has a corresponding yaml file that displays the model architecture. Here is YOLOv5s, defined by yolov5s.yaml
:
Lines 12 to 48 in 1a3ecb8
TensorBoard Graph
Simply start training a model, and then view the TensorBoard Graph for an interactive view of the model architecture. This example shows YOLOv5s viewed in our Notebook β
# Tensorboard
%load_ext tensorboard
%tensorboard --logdir runs/train
# Train YOLOv5s on COCO128 for 3 epochs
python train.py --weights yolov5s.pt --epochs 3
Netron viewer
Use https://netron.app to view exported ONNX models:
python export.py --weights yolov5s.pt --include onnx --simplify
Good luck π and let us know if you have any other questions!
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π Hello, this issue has been automatically marked as stale because it has not had recent activity. Please note it will be closed if no further activity occurs.
Access additional YOLOv5 π resources:
- Wiki β https://github.com/ultralytics/yolov5/wiki
- Tutorials β https://docs.ultralytics.com/yolov5
- Docs β https://docs.ultralytics.com
Access additional Ultralytics β‘ resources:
- Ultralytics HUB β https://ultralytics.com/hub
- Vision API β https://ultralytics.com/yolov5
- About Us β https://ultralytics.com/about
- Join Our Team β https://ultralytics.com/work
- Contact Us β https://ultralytics.com/contact
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 YOLOv5 π and Vision AI β!
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