parameters setting on busi dataset #14
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Maybe there is a problem with my dataset, because some pictures have more than one mask, how do you deal with it? |
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The effect is even worse when multiple masks are taken into account |
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I just picked the first mask i.e _mask |
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how about the parameters setting?running many times but i can't get the result as the same as it in your paper.
Sorry to bother you!
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From: "Jeya Maria ***@***.***>
Date: Sat, Apr 30, 2022 08:02 AM
To: ***@***.***>;
Cc: ***@***.******@***.***>;
Subject: Re: [jeya-maria-jose/UNeXt-pytorch] parameters setting on busidataset (Issue #14)
I just picked the first mask i.e _mask
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I have used the same parameter settings you have specified. Can you please make sure you are using the model after this commit . The previous model file was actually UNeXt-S |
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channel setting i try the code you share(UNeXt-S 8 16 32 64 128) and then set the channel to [16 32 128 160 256]
…---Original---
From: "Jeya Maria ***@***.***>
Date: Sat, Apr 30, 2022 08:09 AM
To: ***@***.***>;
Cc: ***@***.******@***.***>;
Subject: Re: [jeya-maria-jose/UNeXt-pytorch] parameters setting on busi dataset (Issue #14)
I have used the same parameter settings you have specified. Can you please make sure you are using the model after this commit . The previous model file was actually UNeXt-S
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Maybe change the random seed and check average over different train-test splits? |
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i will try again by changing the random seed.(i didn't change the random seed before)
…---Original---
From: "Jeya Maria ***@***.***>
Date: Sat, Apr 30, 2022 08:17 AM
To: ***@***.***>;
Cc: ***@***.******@***.***>;
Subject: Re: [jeya-maria-jose/UNeXt-pytorch] parameters setting on busidataset (Issue #14)
Maybe change the random seed and check average over different train-test splits?
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how about take the all masks into account?
…---Original---
From: "Jeya Maria ***@***.***>
Date: Sat, Apr 30, 2022 08:02 AM
To: ***@***.***>;
Cc: ***@***.******@***.***>;
Subject: Re: [jeya-maria-jose/UNeXt-pytorch] parameters setting on busidataset (Issue #14)
I just picked the first mask i.e _mask
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I encountered the same situation, I used the original author's code with the same hyperparameters as the source code, trained 500 epochs on the BUSI dataset, and the highest IoU on the validation set could only reach about 61.3, and the dice score was about 75.2. Not as high as reported in the paper .... But I divided the training set 8/2 into training and validation sets on the GlaS dataset, and the highest IoU reached 79 on the validation set and 74.51 on the test set, which is higher than the 69.61 reported by your MedT model |
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I also performed poorly on the busi dataset. Do you want to process the data set? What kind of treatment? After I got the dataset, I directly imported it and ran it |
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i tried another random seed and the result can arrive 66－68.You can change the random seed and run again.
…---Original---
From: ***@***.***>
Date: Thu, May 12, 2022 10:07 AM
To: ***@***.***>;
Cc: ***@***.******@***.***>;
Subject: Re: [jeya-maria-jose/UNeXt-pytorch] parameters setting on busidataset (Issue #14)
I also performed poorly on the busi dataset. Do you want to process the data set? What kind of treatment? After I got the dataset, I directly imported it and ran it
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i used the same setting as busi.
On the GLAS, i splited the trainset in 8/2
then tested the whole testset, you can also try it
…---Original---
From: ***@***.***>
Date: Thu, May 12, 2022 10:12 AM
To: ***@***.***>;
Cc: ***@***.******@***.***>;
Subject: Re: [jeya-maria-jose/UNeXt-pytorch] parameters setting on busi dataset (Issue #14)
我尝试了另一个随机种子，结果可以到达66－68。您可以更改随机种子并再次运行。
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***@***.***> 日期：2022 年 5 月 12 日星期四上午 10:07 ***@***.***>; ***@***.***@.>; 主题：回复：busidataset 上的 [jeya-maria-jose/UNeXt-pytorch] 参数设置（问题#14）我在 busi 数据集上的表现也很差。是否要处理数据集？什么样的治疗？拿到数据集后，直接导入运行——直接回复这封邮件，在 GitHub 上查看，或者退订。您收到此消息是因为您编写了该线程。消息 ***@***.******>
Can you show the code in detail? thank you.
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This is not possible, because your test set of data in the training set appeared, the test effect must be good. The training set and the test set can't intersect |
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Sorry, maybe I misrepresented it. the GLAS dataset has a training set and a test set, I just divided the training set 8/2 into a training set and a validation set, and used the data from the test set during the testing phase, not the validation set which belongs to the 20% of the training set. |
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so that was it.I am sorry
…---Original---
From: ***@***.***>
Date: Thu, May 12, 2022 10:26 AM
To: ***@***.***>;
Cc: ***@***.******@***.***>;
Subject: Re: [jeya-maria-jose/UNeXt-pytorch] parameters setting on busi dataset (Issue #14)
Sorry, maybe I misrepresented it. the GLAS dataset has a training set and a test set, I just divided the training set 8/2 into a training set and a validation set, and used the data from the test set during the testing phase, not the validation set which belongs to the 20% of the training set.
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I used the author's latest arch.py submission, and with all other parameters the same, the validation set IoU reached 65.8, which is within the range reported in its paper |
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This is the source code provided by the author， just modifies the random_state parameter value during data partitioning. |
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I just used the same code, same random state and reached 65.8 on busi
…---Original---
From: ***@***.***>
Date: Thu, May 12, 2022 11:07 AM
To: ***@***.***>;
Cc: ***@***.******@***.***>;
Subject: Re: [jeya-maria-jose/UNeXt-pytorch] parameters setting on busidataset (Issue #14)
This is the source code provided by the author， just modifies the random_state parameter value during data partitioning.
train_img_ids, val_img_ids = train_test_split(img_ids, test_size=0.2, random_state=41)
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I want to know the random state you set, my IOU cannot reach 66-68，thanks |
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I will send the code i used within today.If convenience, you can check it. Please bother you to check the email.
Thanks again.
…---Original---
From: "Jeya Maria ***@***.***>
Date: Sat, Apr 30, 2022 08:09 AM
To: ***@***.***>;
Cc: ***@***.******@***.***>;
Subject: Re: [jeya-maria-jose/UNeXt-pytorch] parameters setting on busi dataset (Issue #14)
I have used the same parameter settings you have specified. Can you please make sure you are using the model after this commit . The previous model file was actually UNeXt-S
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亲。能发我一下您修改的达到66-68的效果的代码吗？非常感谢您的帮助！ @小汉科 |
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Hello, I have the same problem for reproduced the 66-68 IOU score based on the BUSI dataset. Could you send me the random state value you used? My email is denebb97@gmail.com . Thanks! |
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非常感谢您的帮助，我很需要您的代码，希望您能分享一下。
…------------------ 原始邮件 ------------------
发件人: "jeya-maria-jose/UNeXt-pytorch" ***@***.***>;
发送时间: 2022年12月20日(星期二) 上午10:47
***@***.***>;
***@***.******@***.***>;
主题: Re: [jeya-maria-jose/UNeXt-pytorch] parameters setting on busi dataset (Issue #14)
I will send the code i used within today.If convenience, you can check it. Please bother you to check the email. Thanks again.
…
---Original--- From: "Jeya Maria @.> Date: Sat, Apr 30, 2022 08:09 AM To: @.>; Cc: @.@.>; Subject: Re: [jeya-maria-jose/UNeXt-pytorch] parameters setting on busi dataset (Issue #14) I have used the same parameter settings you have specified. Can you please make sure you are using the model after this commit . The previous model file was actually UNeXt-S — Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you authored the thread.Message ID: @.***>
Hello, I have the same problem for reproduced the 66-68 IOU score based on the BUSI dataset. Could you send me the random state value you used? thanks!!
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Hi, I did not get the code for the random state values, but I tried some random state values. In my case, the random state value (46) leads to the highest val IoU score of 0.668 . |
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@jeya-maria-jose @YHYeooooong @sdlymywr @sdtegaotian @xiaohancl @liuyx599 ValueError: With n_samples=0, test_size=0.2 and train_size=None, the resulting train set will be empty. Adjust any of the aforementioned parameters. I got this error while using busi dataset. |
also used 46 and got better result, Thank you ! |
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Chinese
…---- 回复的原邮件 ----
| 发件人 | ***@***.***> |
| 日期 | 2023年04月09日 18:30 |
| 收件人 | ***@***.***> |
| 抄送至 | ***@***.***>***@***.***> |
| 主题 | Re: [jeya-maria-jose/UNeXt-pytorch] parameters setting on busi dataset (Issue #14) |
非常感谢您的帮助，我很需要您的代码，希望您能分享一下。
…
------------------ 原始邮件 ------------------ 发件人: "jeya-maria-jose/UNeXt-pytorch" @.>; 发送时间: 2022年12月20日(星期二) 上午10:47 _@**._>; ***@***.***@.>; 主题: Re: [jeya-maria-jose/UNeXt-pytorch] parameters setting on busi dataset (Issue #14) I will send the code i used within today.If convenience, you can check it. Please bother you to check the email. Thanks again. … ---Original--- From: "Jeya Maria @.> Date: Sat, Apr 30, 2022 08:09 AM To: @.>; Cc: @.@.>; Subject: Re: [jeya-maria-jose/UNeXt-pytorch] parameters setting on busi dataset (Issue #14) I have used the same parameter settings you have specified. Can you please make sure you are using the model after this commit . The previous model file was actually UNeXt-S — Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you authored the thread.Message ID: @.> Hello, I have the same problem for reproduced the 66-68 IOU score based on the BUSI dataset. Could you send me the random state value you used? thanks!! — Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you commented.Message ID: @.**_>
Hi, I did not get the code for the random state values, but I tried some random state values. In my case, the random state value (46) leads to the highest val IoU score of 0.668 .
also used 46 and got better result, Thank you !
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me too
…---- 回复的原邮件 ----
| 发件人 | ***@***.***> |
| 日期 | 2023年04月09日 18:30 |
| 收件人 | ***@***.***> |
| 抄送至 | ***@***.***>***@***.***> |
| 主题 | Re: [jeya-maria-jose/UNeXt-pytorch] parameters setting on busi dataset (Issue #14) |
非常感谢您的帮助，我很需要您的代码，希望您能分享一下。
…
------------------ 原始邮件 ------------------ 发件人: "jeya-maria-jose/UNeXt-pytorch" @.>; 发送时间: 2022年12月20日(星期二) 上午10:47 _@**._>; ***@***.***@.>; 主题: Re: [jeya-maria-jose/UNeXt-pytorch] parameters setting on busi dataset (Issue #14) I will send the code i used within today.If convenience, you can check it. Please bother you to check the email. Thanks again. … ---Original--- From: "Jeya Maria @.> Date: Sat, Apr 30, 2022 08:09 AM To: @.>; Cc: @.@.>; Subject: Re: [jeya-maria-jose/UNeXt-pytorch] parameters setting on busi dataset (Issue #14) I have used the same parameter settings you have specified. Can you please make sure you are using the model after this commit . The previous model file was actually UNeXt-S — Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you authored the thread.Message ID: @.> Hello, I have the same problem for reproduced the 66-68 IOU score based on the BUSI dataset. Could you send me the random state value you used? thanks!! — Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you commented.Message ID: @.**_>
Hi, I did not get the code for the random state values, but I tried some random state values. In my case, the random state value (46) leads to the highest val IoU score of 0.668 .
also used 46 and got better result, Thank you !
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Hello, did you run the metrics in the paper, I set the same parameters and couldn't achieve the results in the paper |
Can you send me your code?Because my result is poor.Even though I set it 46.But i need set the val.py 46.Then the result is poor |
Is your val.py also set 46.I do it,but I got a poor result.Can you send me your code? |
I am chinese too.你复现出和作者差不多的结果了吗?可以分享一下你的代码吗？因为我把训练和验证的 random state都设置46了，结果还是比较差 |
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Would you please share you code with me.my result is poor.I am sorry to bother you. |
you dataset name maybe worry |
Could you please share your code? Because mine is less than 65.I am very worried |
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yes ,I also can't achieve this result.But I make some change.With this change I can get better results.So I make some advice?with you that using this code to add yourself code.
…---- Replied Message ----
| From | ***@***.***> |
| Date | 05/30/2023 23:07 |
| To | ***@***.***> |
| Cc | ***@***.***>***@***.***> |
| Subject | Re: [jeya-maria-jose/UNeXt-pytorch] parameters setting on busi dataset (Issue #14) |
Hello, did you run the metrics in the paper, I set the same parameters and couldn't achieve the results in the paper
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你好，可以发我一份代码吗？930773616@qq.com |
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发自我的iPhone
…------------------ Original ------------------
From: Cloud-Liu ***@***.***>
Date: Sun,Jun 18,2023 5:14 PM
To: 577480017 ***@***.***>
Subject: Re: [jeya-maria-jose/UNeXt-pytorch] parameters setting on busidataset (Issue #14)
yes ,I also can't achieve this result.But I make some change.With this change I can get better results.So I make some advice?with you that using this code to add yourself code.
…
---- Replied Message ---- | From | @.> | | Date | 05/30/2023 23:07 | | To | @.> | | Cc | @.***@***.***> | | Subject | Re: [jeya-maria-jose/UNeXt-pytorch] parameters setting on busi dataset (Issue #14) | Hello, did you run the metrics in the paper, I set the same parameters and couldn't achieve the results in the paper — Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you were mentioned.Message ID: @.***>
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May I ask how you made the change? What suggestions do you have?
发自我的iPhone
…------------------ Original ------------------
From: Cloud-Liu ***@***.***>
Date: Sun,Jun 18,2023 5:14 PM
To: 577480017 ***@***.***>
Subject: Re: [jeya-maria-jose/UNeXt-pytorch] parameters setting on busidataset (Issue #14)
yes ,I also can't achieve this result.But I make some change.With this change I can get better results.So I make some advice?with you that using this code to add yourself code.
…
---- Replied Message ---- | From | @.> | | Date | 05/30/2023 23:07 | | To | @.> | | Cc | @.***@***.***> | | Subject | Re: [jeya-maria-jose/UNeXt-pytorch] parameters setting on busi dataset (Issue #14) | Hello, did you run the metrics in the paper, I set the same parameters and couldn't achieve the results in the paper — Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you were mentioned.Message ID: @.***>
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how did you change？
…---Original---
From: ***@***.***>
Date: Sun, Jun 18, 2023 17:10 PM
To: ***@***.***>;
Cc: ***@***.******@***.***>;
Subject: Re: [jeya-maria-jose/UNeXt-pytorch] parameters setting on busidataset (Issue #14)
yes ,I also can't achieve this result.But I make some change.With this change I can get better results.So I make some advice?with you that using this code to add yourself code.
---- Replied Message ----
| From | ***@***.***> |
| Date | 05/30/2023 23:07 |
| To | ***@***.***> |
| Cc | ***@***.***>***@***.***> |
| Subject | Re: [jeya-maria-jose/UNeXt-pytorch] parameters setting on busi dataset (Issue #14) |
Hello, did you run the metrics in the paper, I set the same parameters and couldn't achieve the results in the paper
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你好，我想问一下这个BUSI数据集 是将647张图片混到一个文件夹中训练吗 |
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你好，我想问一下这个BUSI数据集 是将647张图片混到一个文件夹中训练吗 |
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因为那个trai里面有数据集随机划分方式，就是有一行是train_img_ids，val_img_ids=，，，，0.2，，，，，41那里
…---Original---
From: ***@***.***>
Date: Wed, Jan 17, 2024 18:46 PM
To: ***@***.***>;
Cc: ***@***.******@***.***>;
Subject: Re: [jeya-maria-jose/UNeXt-pytorch] parameters setting on busidataset (Issue #14)
你好，我想问一下这个BUSI数据集 是将647张图片混到一个文件夹中训练吗
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Sorry for accidentally turning this off!
My question is I set the parameters on busi dataset as fllows:
epoch:400
batch_size:8
optimizer:Adam
lr:1e-4
momentum:0.9
weight_decay:1e-4
scheduler:ConsineAnnealingLR
channels:[16, 32, 128, 160, 256]
I have resized the images by train_transform and val_transform you provided in the source code.And download the dataset from the link you provided, and then put the benign and malignant (total 647 images)in the folder inputs/busi/images, and the masks in inputs/busi/masks/0
but i got the result IOU:61. (it's 66.95 in the paper)
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