Ultralytics YOLOv5#

@PriyaRanjanReddy yes, you can use YOLOv5nu.pt for training on your dataset. For commercial use, you'll need an Ultralytics Enterprise License, which allows proprietary deployment without AGPL-3.0's open-source requirements. Visit https://ultralytics.com/license for licensing details.

What if i wanted to use yolo11 yolo8 or anything what license should I purchase

and please let me know the cost of the license as well

@PriyaRanjanReddy Ultralytics models, including YOLOv5u, YOLOv8, YOLO11 and YOLO26, are released under AGPL-3.0. You can use them commercially if your application complies with AGPL-3.0 (including open-sourcing it); otherwise you need an Ultralytics Enterprise License. Pricing varies based on your project's needs, so please visit our licensing page to discuss details.

@donia444 it seems that your YOLOv5nu model might be overfitting due to its more complex architecture compared to YOLOv5n, which is simpler and might generalize better on your dataset. Consider using techniques like data augmentation, dropout, or early stopping to mitigate overfitting. You can find additional strategies in the Ultralytics documentation to help address this issue.

@MadhavKrishna1 1. Yes, Kaggle datasets often include label files, but it's best to check the specific dataset details on Kaggle. 2. You can fine-tune the model using the Ultralytics YOLO Python API, which provides built-in support for training and fine-tuning.

@MadhavKrishna1 yOLOv5 models are indeed trained on the full COCO dataset, which includes 80 classes. For additional datasets similar to COCO, you might explore open-source repositories or platforms like Kaggle. Fine-tuning with your specific images should enhance performance on your target data.

or i have use torch.hub.load for it?

WARNING ⚠️ yolov5su.pt appears to require 'ultralytics.yolo', which is not in Ultralytics requirements.

i want to install model locally and use it

@Abhinay-Sadineni yes, yolov5su.pt is an Ultralytics YOLOv5u checkpoint and loads directly with the YOLO class from the ultralytics package, so ensure you have the latest version installed. Checkpoints trained with the original ultralytics/yolov5 repo are the ones that cannot be loaded this way. For more details, visit the YOLOv5 documentation.

is this yolov5su.pt(yolov5 model) compatible with latest ultralytics package ?

my experiment setup doesn't allow the autoinstall 'ultralytics.yolo' (i mean external traffic is blocked)

@glenn-jocher

will the latest package also issue autoinstall warning?

I believe torch.hub.load will also try to install model (which my experiment setup doesn't allow)

Ultralytics 8.2.81 is also giving the same error

To use yolov5su.pt with the latest Ultralytics package, ensure you have the latest version installed. If your setup blocks external traffic, you can manually download the model and dependencies. The autoinstall warning may still appear if dependencies are missing. For a seamless experience, consider using the model with the Ultralytics YOLO class after ensuring all dependencies are locally available.

I built a custom dataset from Ultralytics Hub platform using Yolov5su model type and downloaded the trained model. When I tried to load getting this error : "An error occurred: 'Detect' object has no attribute 'grid'. "

This is how I load the model : yolov5path - points to latest yolov5 git repo. model path - refers to YOLOv5su model trained using Ultralytics Hub.

yolo_model = torch.hub.load(yolov5path, 'custom', force_reload=True, path=model_path, source='local')

YOLOv5 v7.0-338-gff063284 Python-3.12.4 torch-2.3.1+cpu CPU

YOLOv5s summary (fused): 193 layers, 9,114,632 parameters, 0 gradients, 23.8 GFLOPs Adding AutoShape... An error occurred: 'Detect' object has no attribute 'grid'.

@kanagarajan0709 yolov5su.pt is an Ultralytics YOLOv5u checkpoint and loads with the YOLO class from the latest ultralytics package. The "Detect object has no attribute 'grid'" error appears when a checkpoint from the original ultralytics/yolov5 repo is loaded with ultralytics, or when yolov5su.pt is loaded with the old yolov5 repo code. Load yolov5su.pt with from ultralytics import YOLO, and use the yolov5 repo only for checkpoints trained there. See the YOLOv5 documentation.

To resolve this, please ensure you are using the correct version of the YOLOv5 repository that supports the yolov5su.pt model. If the issue persists, consider using the torch.hub.load method with the appropriate YOLOv5 version.

Yes , that's what I would be interested to know about. Could you please let me know which Yolov5 git hub repo that supports loading of yolov5su model.

I am currently on latest git version of this - https://github.com/ultralytics/yolov5. Last commit e8a30cf820b273afbbf1c73a3f1088471a84fade (HEAD -> master, origin/master, origin/HEAD) Author: Glenn Jocher glenn.jocher@ultralytics.com

Also I did use torch.hub.load method to load the model as below , yolo_model = torch.hub.load(yolov5path, 'custom', force_reload=True, path=model_path, source='local')

To load yolov5su.pt, use the YOLO class from the latest ultralytics package (YOLO("yolov5su.pt")). It is an Ultralytics YOLOv5u checkpoint and is not meant for the ultralytics/yolov5 repo, which loads checkpoints trained there.

@Halafz7 thank you for your kind words! 😊

YOLOv5u is designed for object detection. For classification, use a classification model such as YOLO26, which supports it directly.

Python Example

from ultralytics import YOLO

# Load a pretrained YOLO26 classification model
model = YOLO("yolo26n-cls.pt")

# Run inference on an image
results = model("path/to/image.jpg")

# Display the results
results[0].show()

CLI Example

# Run inference with a YOLO26 classification model
yolo predict model=yolo26n-cls.pt source=path/to/image.jpg

For more details, see the Classify docs.

If you have any more questions or need further assistance, feel free to ask! 🚀

Hey there! 😊 It looks like you're exploring the details of our YOLOv5u model. Indeed, YOLOv5u builds upon the YOLOv5 architecture, especially by incorporating an anchor-free, objectness-free split head, a feature inspired by advancements in YOLOv8. This enhancement is aimed at refining the model's ability to detect objects with a better accuracy-speed tradeoff.

Although it seems there might not be detailed standalone documentation on the YOLOv5u model in the Ultralytics GitHub repo, the key updates and features you’re interested in are described in our Ultralytics YOLOv5 Overview. This page highlights the integration of the anchor-free split head into the model and its impact on performance and adaptability in various scenarios.

Regarding your question on the architecture adaptation, yes, YOLOv5u adapts the YOLOv5 v6.1/6.0 architecture. The primary change lies in the detection head of the model, which has moved towards an anchor-free, objectness-free approach. This move enhances flexibility and detection efficiency, especially in complex scenarios where predefined anchor boxes might limit the detection capabilities.

If you're looking for more technical specifics or examples, I'd recommend diving into the architecture description you mentioned. While it focuses on an earlier version, the core architectural principles remain relevant, with the YOLOv5u modifications centered around the detection head for improved performance.

For direct experimentation or further exploration of the YOLOv5u's unique features, playing with the pre-trained models and reviewing their performance metrics might also offer valuable insights.

I hope this clarifies your queries! If you have more questions or need further assistance, feel free to ask. Happy detecting! 🚀

Thank you that is a very helpful answer! Just to make a final clarification. I am using the YOLOv5u in my Masters Thesis and I would like to appropriately record the model I have used. For the YOLOv5xu.pt (the pretrained model), should I refer to it as the extra large version of the YOLOv5u v1.0, or is it the YOLOv5 v7.0, or is there some other versioning that would correctly identify this specific release of the model?

Hey, thanks for reaching out with your question! 😊

For your Masters Thesis, when documenting the model, it's best to refer to it as YOLOv5u, specifying the version as YOLOv5 v7.0 if that's the version you're utilizing, along with the specific variant you're using, for instance, yolov5xu.pt for the extra-large model. It's key to mention both the overall version (v7.0, as an example) and the specific model variant (xu) to provide clear context and specificity about the architecture and features employed in your work.

Here's how you could cite it:

"I utilized the YOLOv5u extra-large variant (yolov5xu.pt) from the YOLOv5 v7.0 release for object detection tasks in my project."

This approach ensures that your documentation is clear, precise, and tracks back to the correct version of the model relevant to your study.

Hope this helps, and all the best with your thesis! 🚀 If there's anything more you need, feel free to ask.