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how the final epoic has higher mAP and the final weights not #11889
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@glenn-jocher @AyushExel @Borda Can you please help me with that? |
@VYRION-Ai sure, I'd be happy to help. Could you please provide more information about the issue you're facing? This will help us better understand and assist you with resolving it. Thank you. |
@glenn-jocher i do training on my dataset , as you in the image the final Epochs has higher map50 , R ,and P
, But the final weight not
|
@VYRION-Ai hello, Based on the information you provided, it seems that in your training, the final Epochs showed higher metric values for map50, R, and P compared to the final weight. The table you shared demonstrates this difference. Although you mentioned an image link, I'm unable to access it in this text-based format. However, if you have any specific questions about this issue or need further assistance, feel free to ask. We're here to help! Thank you. |
@glenn-jocher how the final Epochs showed higher metric values for map50, R, and P compared to the final weight?
and this the normalize final best.pt val is the final Epochs should be consider the best map i got ? |
@VYRION-Ai based on the information you provided, it appears that the final Epochs achieved higher metric values for map50, R, and P compared to the final weight. The table you shared illustrates this difference. In terms of determining the best map, it is generally advisable to consider the metric values from the final Epochs, as they reflect the model's performance after completing the entire training process. However, it's important to note that selecting the best map or determining model performance requires careful evaluation and consideration of various factors, such as dataset, training setup, and specific requirements. If you have any further questions or need additional assistance, please feel free to ask. |
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i do traning on yolov5 and i got this results , now please how the final epoic has higher mAP and the final weights not
look to this image
`Starting training for 100 epochs...
Stopping training early as no improvement observed in last 10 epochs. Best results observed at epoch 69, best model saved as best.pt.
To update EarlyStopping(patience=10) pass a new patience value, i.e.
python train.py --patience 300
or use--patience 0
to disable EarlyStopping.The text was updated successfully, but these errors were encountered: