Unable to get NMS in TFJS exported model #11728
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👋 Hello @Abhishekvats1997, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution. If this is a 🐛 Bug Report, please provide a minimum reproducible example to help us debug it. If this is a custom training ❓ Question, please provide as much information as possible, including dataset image examples and training logs, and verify you are following our Tips for Best Training Results. RequirementsPython>=3.7.0 with all requirements.txt installed including PyTorch>=1.7. To get started: git clone https://github.com/ultralytics/yolov5 # clone
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@Abhishekvats1997 hi there! Thank you for bringing this issue to our attention. I understand that you are experiencing trouble with the To investigate this issue further, it would be helpful if you could provide a minimal reproducible example, including the steps to export the model and any relevant code snippets. This will allow us to better understand the problem and assist you with finding a solution. Furthermore, if you are willing to submit a pull request to address this issue, we greatly appreciate your contribution! Our team will be more than happy to review and merge your changes. Please ensure that your proposed changes follow our contribution guidelines. Thank you again for your report, and we look forward to working with you to address this issue. |
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Code used to export After a bit of digging I could see the reason for this as The "or" here is causing the model conversion to default to class-agnostic NMS in the case of tfjs. After trying to remove this condition I get errors highlighting that the CombinedNonMax Suppression layer is not implemented on TFJs. Is this the reason that you have introduced the above-mentioned "or" condition? If yes, can you suggest a possible solution to use yolov5 on tfjs with per class NMS and limit objects per class other than the manual for loops in JS :( . |
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@Abhishekvats1997 hello! Thank you for bringing this issue to our attention. I understand that you're experiencing difficulty with the After further investigation, it appears that the "or" condition you noticed in the export script is indeed causing the model conversion to default to class-agnostic NMS in the case of TensorFlow.js. Unfortunately, the CombinedNonMax Suppression layer is not implemented in TensorFlow.js, which is why the condition is in place. As of now, there is no straightforward solution to accomplish per-class NMS and limit the number of objects per class in TensorFlow.js without resorting to manual for loops in JavaScript. It would involve implementing custom JavaScript code for performing per-class NMS and enforcing the object limits. We understand that this could lead to potential performance overhead due to the need for additional computation in JavaScript. If this functionality is critical for your use case, we recommend considering alternative options such as using Python or exploring other object detection frameworks that support per-class NMS and object limits in TensorFlow.js. We hope this information clarifies the situation. Please let us know if you have any further questions or concerns. We appreciate your interest in YOLOv5 and the Ultralytics team's work. Thank you for your understanding and contribution to the YOLO community! |
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👋 Hello there! We wanted to give you a friendly reminder that this issue has not had any recent activity and may be closed soon, but don't worry - you can always reopen it if needed. If you still have any questions or concerns, please feel free to let us know how we can help. For additional resources and information, please see the links below:
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YOLOv5 Component
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Bug
I tried exporting my trained .pt model to tfjs. The --nms flag and --topk-per-class flags do not seem to have any effect on the exported model.
Environment
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Minimal Reproducible Example
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Are you willing to submit a PR?
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