corrupt JPEG data: premature end od data segment | training yolov8m #16389
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👋 Hello @Vinaygoudasp7, thank you for reaching out and for your interest in Ultralytics 🚀! We recommend checking out our Docs for additional guidance. If you’re new, there are helpful Python and CLI usage examples that might address common questions. For your JPEG issue, it seems like you might be encountering a 🐛 bug. Please provide a minimum reproducible example to assist us in debugging this issue effectively. This will help a lot! Regarding the training conversion issue, could you ensure that your calibration set is properly normalized and verify your batch-normalization settings during training? 🤔 Join the Ultralytics community for real-time support or discussions. Check out our Discord 🎧, engage in our Discourse, or explore conversations in our Subreddit. UpgradeMake sure you're running the latest version of the pip install -U ultralyticsEnvironmentsHere's where YOLOv8 can be executed seamlessly:
StatusEnsure the systems are operational by checking our Ultralytics CI tests! ✅ Please note, this is an automated response. An Ultralytics engineer will follow up with you soon. Thank you for your patience and understanding! 😃 |
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I don't think it's related to the corrupted JPEG. It's probably failing for some other reason. |
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yes now i have deleted the corrupted jpeg and retrain with some epoch to test but still it geting same error @Y-T-G @glenn-jocher |
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You should ask it on Hailo GitHub probably since it's not ultralytics related. |
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@glenn-jocher i geting this error corrupt JPEG data: premature end of data segment while traing yolov8m and it showing this error on each epoch bvut it complete 700 epoch but when i used this model for converting into hailo it showing the error that NegativeSlopeExponentNonFixable: Quantization failed in layer custom_violation_model_700epoch/conv83 due to unsupported required slope. Desired shift is 13.0, but op has only 8 data bits. This error raises when the data or weight range are not balanced. Mostly happens when using random calibration-set/weights, the calibration-set is not normalized properly or batch-normalization was not used during training.
so plz i am requesting you to give solution for this
thank you
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