Whoa, training YOLOv10 on a dataset packed with 110k SKUs sounds intense! Any tips on dealing with false positives when objects look super similar, or do we just teach the model to "shelf-assume" nothing?
Training on the SKU-110k dataset can be challenging due to similar-looking objects. To reduce false positives, consider refining your model with more specific annotations and augmenting your dataset to improve diversity. Fine-tuning hyperparameters and using advanced techniques like non-maximum suppression can also help. For more details, check out the SKU-110k documentation: docs.ultralytics.com/datasets/detect/sku-110k/ 😊
You're welcome! Glad you found it helpful! 😊 If you want to dive deeper, check out the full YOLOv10 documentation here: docs.ultralytics.com/models/yolov10/ 🚀
Hi, thank you for this amazing and detailed explanation! Your work is incredibly helpful and inspiring. I would deeply appreciate it if you could share the trained model after the training process. It would make a significant difference in advancing my project and learning. Thank you so much for your generosity and support!
Thank you for your kind words! 😊 While I can't share trained models directly, I recommend checking out the Ultralytics HUB hub.ultralytics.com/ for easy access to pre-trained models and tools to train your own. It's a great platform to advance your projects without needing extensive coding knowledge. Happy exploring!
You're welcome! Glad we could help. For more detailed information on YOLOv10, check out our documentation here: docs.ultralytics.com/models/yolov10/. Good luck with your research! 😊
Absolutely riveting tutorial! Considering the high similarity of objects and the dense packing in the SKU-110k dataset, how does YOLOv10 handle potential false positives or the misclassification of adjacent items? Are there specific techniques within the Ultralytics package that enhance precision in such nuanced scenarios?
Thank you! 😊 YOLOv10's architecture, especially its NMS-free approach and dual assignment strategies, significantly reduces false positives and improves precision. The model's enhanced feature extraction and multiscale feature fusion also help in distinguishing closely packed objects. For more details, check out the YOLOv10 documentation docs.ultralytics.com/models/yolov10/.
Great walkthrough! Quick Q: Given that the SKU-110k dataset has densely packed and visually similar objects, how do you handle potential overlapping predictions during fine-tuning? Any tips on tweaking confidence thresholds or NMS settings to enhance detection accuracy? Let's get those retail shelves looking spotless!
Thanks for the kind words! 😊 For handling overlapping predictions in densely packed scenes like SKU-110k, tweaking the Non-Maximum Suppression (NMS) settings and confidence thresholds can indeed help. Here are a few tips: 1. Adjust Confidence Threshold: Lowering the confidence threshold can help detect more objects, but be cautious of false positives. 2. Tune NMS IoU Threshold: Adjust the IoU threshold for NMS to balance between detecting overlapping objects and avoiding duplicate detections. You can modify these settings directly in your training script or configuration file. For more details, check out the SKU-110k documentation docs.ultralytics.com/datasets/detect/sku-110k/. Happy training! 🛒📈
Great tutorial on YOLOv10 with SKU-110k! How do you handle potential biases in object detection when training on datasets with such visually similar categories? Are there strategies you've found effective to improve model generalization without losing accuracy?
Thanks for watching! 😊 Handling biases in object detection, especially with visually similar categories, can be challenging. Strategies like data augmentation, using diverse datasets, and fine-tuning hyperparameters can help improve generalization. Regularly testing your model is also crucial. For more insights, check out our model testing guide here: docs.ultralytics.com/guides/model-testing/
Can YOLOv10 handle those sneaky camo patterns or mock-up retailers love to use? Just curious bro, seems like it could have some fun applications for outdoor gear shops. Thoughts?
Absolutely! YOLOv10 is designed to handle complex patterns and textures, making it a great fit for detecting camo patterns or mock-ups in retail settings. Its advanced algorithms excel in real-time object detection, even in challenging scenarios. For more details on its capabilities, check out the YOLOv10 documentation docs.ultralytics.com/models/yolov10/. Happy detecting! 😊
Absolutely fascinating walkthrough! Quick question: Given the dense nature of SKUs in retail images, how does YOLOv10 handle overlapping objects compared to its predecessors? Would love to hear your thoughts on any specific improvements made in handling such complexities. #RetailTechTalks 🚀
Great question! YOLOv10 introduces several improvements over its predecessors, particularly in handling densely packed and overlapping objects. One key enhancement is the elimination of Non-Maximum Suppression (NMS), which helps in better distinguishing closely positioned objects. Additionally, YOLOv10 leverages advanced feature extraction techniques and improved anchor-free detection, which enhances its ability to detect and classify overlapping objects more accurately. For more details, check out the YOLOv10 documentation docs.ultralytics.com/models/yolov10/. 🚀 Feel free to explore the SKU-110k dataset documentation docs.ultralytics.com/datasets/detect/sku-110k/ for more insights on training models with densely packed retail images.
Bom dia amigos da detecção! Intrigued about the SKU-110k dataset described as "densely packed"-how does YOLOv10 handle misdetections in such crowded scenes? Can fine-tuning help significantly reduce false positives without losing detection speed, or does it become slower post-adjustment? 🎶 Always love when tech experiments with retail nuances!
Bom dia! 😊 YOLOv10 is designed to handle densely packed scenes like those in the SKU-110k dataset quite effectively. Fine-tuning can indeed help reduce false positives by adjusting the model to better understand the specific nuances of your dataset. This process typically involves tweaking hyperparameters and training on a subset of your data. While fine-tuning can improve accuracy, it generally doesn't significantly impact detection speed if done correctly. However, it's always a balance between precision and speed. For more details on fine-tuning and using the SKU-110k dataset, check out our documentation: SKU-110k Dataset docs.ultralytics.com/datasets/detect/sku-110k/. Happy experimenting! 🚀
Thank you for sharing, It helped me a lot. I have a question though. I am currently working on a project and I need a dataset in a retail environment(Supermarket) including humans, because my target is to detect humans in supermarkets. I only found the MERL dataset with these characteristics , but the scenes are not crowded enough. I was wondering if you know such a dataset and could help me?
I'm glad the video helped! For detecting humans in crowded retail environments, you might want to explore the SKU-110k dataset, which focuses on densely packed retail shelves. While it primarily targets products, it could be a starting point. You can find more details here: docs.ultralytics.com/datasets/detect/sku-110k/. For datasets specifically including humans, you might also consider looking into datasets like COCO or Open Images, which have a wide range of annotated images.
Amidst the dance of algorithms on retail shelves, have you found any particular challenges in distinguishing between similar SKU categories with YOLOv10-especially when retail seems a chaotic art gallery? How might this complexity affect real-world deployment in stores bustling with diversity and disarray?
Great question! The SKU-110k dataset indeed presents challenges due to the densely packed and similar-looking items. YOLOv10's advanced architecture helps improve detection accuracy, but distinguishing similar SKUs can still be tricky. In real-world deployments, this complexity might require additional fine-tuning and possibly integrating other data sources or sensors to enhance accuracy. For more insights, check out the SKU-110k documentation: SKU-110k Dataset docs.ultralytics.com/datasets/detect/sku-110k/. 😊
Yo, is YOLOv10 really the knight in shining armor for retail shelf chaos, or are there pitfalls when it encounters really similar-looking products? Spill the tea on those tricky grocery aisles!!! 🛒
Hey there! YOLOv10 is definitely a powerful tool for tackling retail shelf chaos, thanks to its advanced detection capabilities. However, it can still face challenges with very similar-looking products due to subtle differences. Fine-tuning the model on specific datasets like SKU-110k can help improve accuracy. Check out the YOLOv10 docs for more insights: docs.ultralytics.com/models/yolov10/ 😊
To test the best fold with a test set, you can follow these steps: 1. Train Each Fold: Complete training for all folds using K-Fold Cross Validation. 2. Evaluate Performance: Compare the performance metrics (e.g., accuracy, mAP) of each fold. 3. Select Best Fold: Identify the fold with the best performance. 4. Test with Test Set: Use the model from the best-performing fold to evaluate on your separate test set. Ensure your test set is not used during training or validation to maintain unbiased evaluation. For more details, check out our K-Fold Cross Validation guide docs.ultralytics.com/guides/kfold-cross-validation/.
You're right! The guide primarily focuses on training and validation. To test the best fold, you can load the model weights from the best-performing fold and run inference on your test set. Here's a simple approach: 1. Save Best Model Weights: During training, save the model weights for each fold. 2. Load Best Weights: After identifying the best fold, load its weights. 3. Run Inference: Use the loaded model to run inference on your test set. This ensures you evaluate the model's performance on unseen data. For more details, you can refer to the Ultralytics documentation. 😊
It sounds like you might be hitting a limit set elsewhere in your configuration. Make sure your model and dataset settings allow for more detections. Also, ensure you're using the latest versions of `ultralytics` and `torch`. If the issue persists, you might want to check the inference script for any hardcoded limits. For more details, check the Ultralytics documentation docs.ultralytics.com/.
Thanks for sharing your experience! Make sure you're using the latest versions of `torch` and `ultralytics`. Also, consider fine-tuning hyperparameters and augmentations. For more optimization tips, check out our YOLOv10 documentation docs.ultralytics.com/models/yolov10/. If you need further assistance, feel free to provide more details! 😊
Thanks for pointing that out! You can find the YOLOv10 configuration files in the Ultralytics GitHub repository. If you need more details, check out the YOLOv10 documentation docs.ultralytics.com/models/yolov10/. If you have any specific issues, feel free to share more details! 😊
Whoa, training YOLOv10 on a dataset packed with 110k SKUs sounds intense! Any tips on dealing with false positives when objects look super similar, or do we just teach the model to "shelf-assume" nothing?
Training on the SKU-110k dataset can be challenging due to similar-looking objects. To reduce false positives, consider refining your model with more specific annotations and augmenting your dataset to improve diversity. Fine-tuning hyperparameters and using advanced techniques like non-maximum suppression can also help. For more details, check out the SKU-110k documentation: docs.ultralytics.com/datasets/detect/sku-110k/ 😊
Awesome YOLOv10 rundown, thanks Nicolai!
You're welcome! Glad you found it helpful! 😊 If you want to dive deeper, check out the full YOLOv10 documentation here: docs.ultralytics.com/models/yolov10/ 🚀
Hi, thank you for this amazing and detailed explanation! Your work is incredibly helpful and inspiring. I would deeply appreciate it if you could share the trained model after the training process. It would make a significant difference in advancing my project and learning. Thank you so much for your generosity and support!
Thank you for your kind words! 😊 While I can't share trained models directly, I recommend checking out the Ultralytics HUB hub.ultralytics.com/ for easy access to pre-trained models and tools to train your own. It's a great platform to advance your projects without needing extensive coding knowledge. Happy exploring!
thank you so much i have a research assignment on yolov10 and have been looking for it for days
You're welcome! Glad we could help. For more detailed information on YOLOv10, check out our documentation here: docs.ultralytics.com/models/yolov10/. Good luck with your research! 😊
Absolutely riveting tutorial! Considering the high similarity of objects and the dense packing in the SKU-110k dataset, how does YOLOv10 handle potential false positives or the misclassification of adjacent items? Are there specific techniques within the Ultralytics package that enhance precision in such nuanced scenarios?
Thank you! 😊 YOLOv10's architecture, especially its NMS-free approach and dual assignment strategies, significantly reduces false positives and improves precision. The model's enhanced feature extraction and multiscale feature fusion also help in distinguishing closely packed objects. For more details, check out the YOLOv10 documentation docs.ultralytics.com/models/yolov10/.
Great walkthrough! Quick Q: Given that the SKU-110k dataset has densely packed and visually similar objects, how do you handle potential overlapping predictions during fine-tuning? Any tips on tweaking confidence thresholds or NMS settings to enhance detection accuracy? Let's get those retail shelves looking spotless!
Thanks for the kind words! 😊 For handling overlapping predictions in densely packed scenes like SKU-110k, tweaking the Non-Maximum Suppression (NMS) settings and confidence thresholds can indeed help. Here are a few tips:
1. Adjust Confidence Threshold: Lowering the confidence threshold can help detect more objects, but be cautious of false positives.
2. Tune NMS IoU Threshold: Adjust the IoU threshold for NMS to balance between detecting overlapping objects and avoiding duplicate detections.
You can modify these settings directly in your training script or configuration file. For more details, check out the SKU-110k documentation docs.ultralytics.com/datasets/detect/sku-110k/.
Happy training! 🛒📈
Great tutorial on YOLOv10 with SKU-110k! How do you handle potential biases in object detection when training on datasets with such visually similar categories? Are there strategies you've found effective to improve model generalization without losing accuracy?
Thanks for watching! 😊 Handling biases in object detection, especially with visually similar categories, can be challenging. Strategies like data augmentation, using diverse datasets, and fine-tuning hyperparameters can help improve generalization. Regularly testing your model is also crucial. For more insights, check out our model testing guide here: docs.ultralytics.com/guides/model-testing/
Thanks for sharing this 👍
You're welcome! Glad you enjoyed it! If you have any questions or need further information, feel free to ask. 😊
Can YOLOv10 handle those sneaky camo patterns or mock-up retailers love to use? Just curious bro, seems like it could have some fun applications for outdoor gear shops. Thoughts?
Absolutely! YOLOv10 is designed to handle complex patterns and textures, making it a great fit for detecting camo patterns or mock-ups in retail settings. Its advanced algorithms excel in real-time object detection, even in challenging scenarios. For more details on its capabilities, check out the YOLOv10 documentation docs.ultralytics.com/models/yolov10/. Happy detecting! 😊
Absolutely fascinating walkthrough! Quick question: Given the dense nature of SKUs in retail images, how does YOLOv10 handle overlapping objects compared to its predecessors? Would love to hear your thoughts on any specific improvements made in handling such complexities. #RetailTechTalks 🚀
Great question! YOLOv10 introduces several improvements over its predecessors, particularly in handling densely packed and overlapping objects. One key enhancement is the elimination of Non-Maximum Suppression (NMS), which helps in better distinguishing closely positioned objects. Additionally, YOLOv10 leverages advanced feature extraction techniques and improved anchor-free detection, which enhances its ability to detect and classify overlapping objects more accurately. For more details, check out the YOLOv10 documentation docs.ultralytics.com/models/yolov10/. 🚀
Feel free to explore the SKU-110k dataset documentation docs.ultralytics.com/datasets/detect/sku-110k/ for more insights on training models with densely packed retail images.
Bom dia amigos da detecção! Intrigued about the SKU-110k dataset described as "densely packed"-how does YOLOv10 handle misdetections in such crowded scenes? Can fine-tuning help significantly reduce false positives without losing detection speed, or does it become slower post-adjustment? 🎶 Always love when tech experiments with retail nuances!
Bom dia! 😊 YOLOv10 is designed to handle densely packed scenes like those in the SKU-110k dataset quite effectively. Fine-tuning can indeed help reduce false positives by adjusting the model to better understand the specific nuances of your dataset. This process typically involves tweaking hyperparameters and training on a subset of your data.
While fine-tuning can improve accuracy, it generally doesn't significantly impact detection speed if done correctly. However, it's always a balance between precision and speed. For more details on fine-tuning and using the SKU-110k dataset, check out our documentation: SKU-110k Dataset docs.ultralytics.com/datasets/detect/sku-110k/.
Happy experimenting! 🚀
Thank you for sharing, It helped me a lot. I have a question though. I am currently working on a project and I need a dataset in a retail environment(Supermarket) including humans, because my target is to detect humans in supermarkets. I only found the MERL dataset with these characteristics , but the scenes are not crowded enough. I was wondering if you know such a dataset and could help me?
I'm glad the video helped! For detecting humans in crowded retail environments, you might want to explore the SKU-110k dataset, which focuses on densely packed retail shelves. While it primarily targets products, it could be a starting point. You can find more details here: docs.ultralytics.com/datasets/detect/sku-110k/. For datasets specifically including humans, you might also consider looking into datasets like COCO or Open Images, which have a wide range of annotated images.
@@Ultralytics I will definitely check them. Thank you very much.
You're welcome! If you have any more questions, feel free to ask. Happy experimenting! 😊
@@Ultralytics Thank you very much. I sure will.😍
You're welcome! Enjoy your project! 🚀
Amidst the dance of algorithms on retail shelves, have you found any particular challenges in distinguishing between similar SKU categories with YOLOv10-especially when retail seems a chaotic art gallery? How might this complexity affect real-world deployment in stores bustling with diversity and disarray?
Great question! The SKU-110k dataset indeed presents challenges due to the densely packed and similar-looking items. YOLOv10's advanced architecture helps improve detection accuracy, but distinguishing similar SKUs can still be tricky. In real-world deployments, this complexity might require additional fine-tuning and possibly integrating other data sources or sensors to enhance accuracy. For more insights, check out the SKU-110k documentation: SKU-110k Dataset docs.ultralytics.com/datasets/detect/sku-110k/. 😊
Yo, is YOLOv10 really the knight in shining armor for retail shelf chaos, or are there pitfalls when it encounters really similar-looking products? Spill the tea on those tricky grocery aisles!!! 🛒
Hey there! YOLOv10 is definitely a powerful tool for tackling retail shelf chaos, thanks to its advanced detection capabilities. However, it can still face challenges with very similar-looking products due to subtle differences. Fine-tuning the model on specific datasets like SKU-110k can help improve accuracy. Check out the YOLOv10 docs for more insights: docs.ultralytics.com/models/yolov10/ 😊
I am using k-fold and training each fold. but i dont know how to test the best fold with test set ?
To test the best fold with a test set, you can follow these steps:
1. Train Each Fold: Complete training for all folds using K-Fold Cross Validation.
2. Evaluate Performance: Compare the performance metrics (e.g., accuracy, mAP) of each fold.
3. Select Best Fold: Identify the fold with the best performance.
4. Test with Test Set: Use the model from the best-performing fold to evaluate on your separate test set.
Ensure your test set is not used during training or validation to maintain unbiased evaluation.
For more details, check out our K-Fold Cross Validation guide docs.ultralytics.com/guides/kfold-cross-validation/.
@ there is no explanation or implementation for testing only validation
You're right! The guide primarily focuses on training and validation. To test the best fold, you can load the model weights from the best-performing fold and run inference on your test set. Here's a simple approach:
1. Save Best Model Weights: During training, save the model weights for each fold.
2. Load Best Weights: After identifying the best fold, load its weights.
3. Run Inference: Use the loaded model to run inference on your test set.
This ensures you evaluate the model's performance on unseen data. For more details, you can refer to the Ultralytics documentation. 😊
why does max_det=1000 not work during inference? setting to 1000 still results in only 300 targets
It sounds like you might be hitting a limit set elsewhere in your configuration. Make sure your model and dataset settings allow for more detections. Also, ensure you're using the latest versions of `ultralytics` and `torch`. If the issue persists, you might want to check the inference script for any hardcoded limits. For more details, check the Ultralytics documentation docs.ultralytics.com/.
I tried already compared to yolo v8 it is giving less mAP on sku110k
Thanks for sharing your experience! Make sure you're using the latest versions of `torch` and `ultralytics`. Also, consider fine-tuning hyperparameters and augmentations. For more optimization tips, check out our YOLOv10 documentation docs.ultralytics.com/models/yolov10/. If you need further assistance, feel free to provide more details! 😊
yolov10 yaml files are not avilable
Thanks for pointing that out! You can find the YOLOv10 configuration files in the Ultralytics GitHub repository. If you need more details, check out the YOLOv10 documentation docs.ultralytics.com/models/yolov10/. If you have any specific issues, feel free to share more details! 😊