Awesome tutorial! I'm curious about how robust the model is in diverse real-world scenarios, like different hand positions or occlusions. Has anyone experimented with pushing the model to these limits? #YOLOGoesWild
Thanks for the enthusiasm! 😊 Ultralytics YOLO models, including YOLO11, are designed to handle diverse scenarios, but performance can vary based on the complexity of the environment, like occlusions or unusual hand positions. Experimenting with different datasets and scenarios can help assess robustness. If you're interested in testing these limits, consider using the Ultralytics HUB for easy experimentation. Feel free to share your findings with the community! 🚀 Check out more on YOLO11 here: docs.ultralytics.com/models/yolo11/
Amazing tutorial on hand keypoints with YOLO11! I'm curious about how robust the model is in real-world conditions, such as varying lighting and hand positions. Could this method potentially be augmented for emotion recognition or other applications beyond mere skeletal tracking?
Thanks for watching! 😊 YOLO11 is designed to be robust, handling varying conditions like lighting and hand positions effectively. Its flexibility allows for potential applications beyond skeletal tracking, such as emotion recognition by analyzing gestures and expressions. For more on YOLO11's capabilities, check out our pose estimation guide www.ultralytics.com/blog/how-to-use-ultralytics-yolo11-for-pose-estimation.
Yo, this is slick! But quick Q - how does YOLO11 handle overlapping hands in complex poses? Like would it still accurately pinpoint individual keypoints, or does it start bugging out? Thinking about applying this for sports training visuals… hands be everywhere in those replays!!!
Great question! YOLO11 is designed to handle overlapping hands effectively by leveraging its high-precision keypoint detection and pose estimation capabilities. Using advanced models and datasets like the Hand Keypoints Dataset docs.ultralytics.com/datasets/pose/hand-keypoints/, it can output 21 keypoints per hand even in challenging scenarios like overlaps or occlusion. The model intelligently identifies each hand's structure and position based on context, ensuring accurate tracking for sports visuals or dynamic movements. For more details, check out the Pose Estimation Guide docs.ultralytics.com/tasks/pose/ to explore its robustness in different scenarios! 🏀✋
In the whimsical dance of pixels and predictions, could anyone elaborate on how Ultralytics YOLO11 compares with previous iterations in handling subtle, intricate hand gestures? Also, might there be thresholds where its fanciful dance falters, particularly in low-lit phantom corners or with occluded, overlapping fingers that play hide-and-seek beats of a hand-waltz?
YOLO11 excels in capturing intricate details, thanks to its enhanced feature extraction and higher mean Average Precision (mAP) compared to previous versions like YOLOv8. This makes it particularly adept at recognizing subtle hand gestures. However, like any model, it may face challenges in low-light conditions or with occluded fingers, where overlapping elements can reduce detection accuracy. For best results, ensure good lighting and clear visibility of the hand. For more insights, check out our YOLO11 documentation docs.ultralytics.com/models/yolo11/. 🌟
Thanks for the feedback! If you're encountering corrupt image warnings, try checking your dataset for any issues or re-download it if possible. For the Weights & Biases API, ensure you've set it up correctly in your environment. You can find more setup details here: docs.ultralytics.com/integrations/weights-biases/. Let us know if you need further assistance! 😊
@Ultralytics I don't have any issues. But if you look through this video, it getting warnings and.... I think the guy in the video doesn't care about validity of running codes. tnx btw
Thanks for pointing that out! We strive to ensure our tutorials are as accurate and helpful as possible. I'll pass your feedback on to the team to improve future content. If you have any other suggestions or questions, feel free to share. 😊
Thanks for sharing this amazing tutorial
You're welcome! I'm glad you enjoyed it! If you have any questions or need further information, feel free to ask. 😊
Awesome tutorial! I'm curious about how robust the model is in diverse real-world scenarios, like different hand positions or occlusions. Has anyone experimented with pushing the model to these limits? #YOLOGoesWild
Thanks for the enthusiasm! 😊 Ultralytics YOLO models, including YOLO11, are designed to handle diverse scenarios, but performance can vary based on the complexity of the environment, like occlusions or unusual hand positions. Experimenting with different datasets and scenarios can help assess robustness. If you're interested in testing these limits, consider using the Ultralytics HUB for easy experimentation. Feel free to share your findings with the community! 🚀 Check out more on YOLO11 here: docs.ultralytics.com/models/yolo11/
Amazing tutorial on hand keypoints with YOLO11! I'm curious about how robust the model is in real-world conditions, such as varying lighting and hand positions. Could this method potentially be augmented for emotion recognition or other applications beyond mere skeletal tracking?
Thanks for watching! 😊 YOLO11 is designed to be robust, handling varying conditions like lighting and hand positions effectively. Its flexibility allows for potential applications beyond skeletal tracking, such as emotion recognition by analyzing gestures and expressions. For more on YOLO11's capabilities, check out our pose estimation guide www.ultralytics.com/blog/how-to-use-ultralytics-yolo11-for-pose-estimation.
Yo, this is slick! But quick Q - how does YOLO11 handle overlapping hands in complex poses? Like would it still accurately pinpoint individual keypoints, or does it start bugging out? Thinking about applying this for sports training visuals… hands be everywhere in those replays!!!
Great question! YOLO11 is designed to handle overlapping hands effectively by leveraging its high-precision keypoint detection and pose estimation capabilities. Using advanced models and datasets like the Hand Keypoints Dataset docs.ultralytics.com/datasets/pose/hand-keypoints/, it can output 21 keypoints per hand even in challenging scenarios like overlaps or occlusion. The model intelligently identifies each hand's structure and position based on context, ensuring accurate tracking for sports visuals or dynamic movements.
For more details, check out the Pose Estimation Guide docs.ultralytics.com/tasks/pose/ to explore its robustness in different scenarios! 🏀✋
In the whimsical dance of pixels and predictions, could anyone elaborate on how Ultralytics YOLO11 compares with previous iterations in handling subtle, intricate hand gestures? Also, might there be thresholds where its fanciful dance falters, particularly in low-lit phantom corners or with occluded, overlapping fingers that play hide-and-seek beats of a hand-waltz?
YOLO11 excels in capturing intricate details, thanks to its enhanced feature extraction and higher mean Average Precision (mAP) compared to previous versions like YOLOv8. This makes it particularly adept at recognizing subtle hand gestures. However, like any model, it may face challenges in low-light conditions or with occluded fingers, where overlapping elements can reduce detection accuracy. For best results, ensure good lighting and clear visibility of the hand. For more insights, check out our YOLO11 documentation docs.ultralytics.com/models/yolo11/. 🌟
You got so many warnings for corrupt images. Also didn't put wandb api. WEAK
Thanks for the feedback! If you're encountering corrupt image warnings, try checking your dataset for any issues or re-download it if possible. For the Weights & Biases API, ensure you've set it up correctly in your environment. You can find more setup details here: docs.ultralytics.com/integrations/weights-biases/. Let us know if you need further assistance! 😊
@Ultralytics I don't have any issues. But if you look through this video, it getting warnings and.... I think the guy in the video doesn't care about validity of running codes. tnx btw
Thanks for pointing that out! We strive to ensure our tutorials are as accurate and helpful as possible. I'll pass your feedback on to the team to improve future content. If you have any other suggestions or questions, feel free to share. 😊