Stunning overview of YOLOv9! I'm curious, amidst training on crack segmentation datasets, how does YOLOv9 handle real-world variations and surface irregularities in infrastructure, and are there unexpected challenges or limitations you’ve observed?
Great question! YOLOv9 is designed to handle real-world variations and surface irregularities effectively due to its robust architecture and advanced segmentation capabilities. However, challenges like varying lighting conditions, occlusions, and diverse crack patterns can impact performance. Ensuring a diverse and well-annotated dataset, like the Roboflow Crack Segmentation Dataset, helps mitigate these issues. For more details on using this dataset, check out our documentation: docs.ultralytics.com/datasets/segment/crack-seg/.
Really insightful breakdown! For real-world crack segmentation, how well does YOLOv9 handle different types of surfaces or lighting conditions? Also, can its robustness be extended to industrial applications like bridge inspections and aerospace component monitoring? Asking for my skynet dream. The enthusiasm's lighting up my ML heart, let's GPT-sync some ideas out! 🚀💥
Glad you enjoyed the breakdown! 😊 YOLOv9 is designed to handle diverse conditions, including varying surfaces and lighting, thanks to its robust architecture. It's definitely applicable to industrial tasks like bridge inspections and aerospace monitoring, where precision is key. For more on using YOLOv9 in such applications, check out the Crack Segmentation Dataset docs.ultralytics.com/datasets/segment/crack-seg/. Keep dreaming big with your skynet vision! 🚀
With YOLOv9's impressive segmentation capabilities shown here, how do you think it would handle more ambiguous objects, like mist or shadows? Could it potentially redefine how we deal with such challenging scenarios in real-world applications?
Great question! YOLOv9's segmentation capabilities are indeed impressive, but handling ambiguous objects like mist or shadows can be challenging for any model. The performance would largely depend on the quality and diversity of the training data. If the dataset includes varied examples of mist and shadows, YOLOv9 could potentially learn to segment these effectively. For real-world applications, continuous model training and fine-tuning with specific datasets are key. Make sure you're using the latest versions of `torch` and `ultralytics` for the best performance. For more details, check out our documentation at docs.ultralytics.com. 🚀
Hi there! 👋 Great question! The ability to detect cracks as small as 1mm or 1cm largely depends on the resolution of your input images and the quality of your dataset. Ensure your images are high-resolution and that the cracks are clearly visible. Also, make sure you're using the latest versions of `torch` and `ultralytics` for the best performance. For more details, you can check out our documentation on crack segmentation here: Crack Segmentation Dataset docs.ultralytics.com/datasets/segment/crack-seg/. If you have any specific issues or need further assistance, feel free to share more details! 😊 Happy detecting! 🚀
You can download the dataset using the link: ultralytics.com/assets/crack-seg.zip Once downloaded, make sure to zip the file and you will able to see the train and val folder, which includes images as well as annotations. we hope this will help :) Thanks, Ultralytics Team!
1) I think most people would like utilize webcam , rather than indiv images. 2) do i need to get API key from roboflow? I'd rather not have to. Is it free? unlimited?
Hi there! 😊 1) Absolutely, using a webcam is a popular choice! You can easily run YOLOv9 with your webcam by specifying the source as `0` in the CLI, like this: ` yolo predict model=yolov9.pt source=0 ` 2) You don't need an API key from Roboflow to use YOLOv9. Roboflow offers free tiers, but for unlimited access, you might need a paid plan. More details can be found on their pricing page roboflow.com/pricing. For more info, check out our documentation docs.ultralytics.com. If you have any more questions, feel free to ask! Happy detecting! 🚀
I'm simply trying to learn the process. Can you do examples that only include under 100 images please? and only 10 epoch? When it does not work, and i've wasted hours, its frustrating.
Hi there! 😊 Thanks for your comment. For a quick and efficient learning experience, you can definitely train YOLOv9 with a small dataset and fewer epochs. Here's a simple command to get you started: ` yolo train data=your_dataset.yaml model=yolov9.pt epochs=10 imgsz=640 ` Make sure you're using the latest versions of `torch` and `ultralytics`. You can find more details in our documentation docs.ultralytics.com. If you encounter any issues, feel free to share specific error messages or code snippets so we can assist you better. Happy training! 🚀 For more tutorials and examples, check out our Ultralytics Docs docs.ultralytics.com.
We are currently working on integrating YOLOv10 and will release it as soon as possible, though we do not have a confirmed date yet. To stay updated with our community and new features in Ultralytics, you can follow us on these platforms: LinkedIn: www.linkedin.com/company/ultralytics/ Twitter: twitter.com/ultralytics GitHub: github.com/ultralytics/ultralytics Thanks, Ultralytics Team!
@@Ultralytics The new architectural improvements and unified model design suggest a significant enhancement in integrating multiple classification types without needing separate models. Can we expect a pre-trained model that encompasses object detection, instance segmentation, and key-point detection, or a combination of these capabilities, all within a single pre-trained model being released by Ultralytics? Have there been any discussions at Ultralytics or are there any known plans for a multiple classification pre-trained model release?
Thank you for your feedback. Currently, we do not have plans to release a single model that performs detection, segmentation, and pose estimation. However, we will consider this idea internally and share any updates if discussions progress. Stay tuned! Thanks, Ultralytics Team!
Sure! You can find the detailed guide for using YOLOv9 with Google Colab here: docs.ultralytics.com/integrations/google-colab/. This guide will help you set up and train your YOLOv9 model efficiently. Enjoy! 😊
