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IcedDoggie / Micro-Expression-with-Deep-Learning

Experimentation of deep learning on the subjects of micro-expression spotting and recognition.
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The result did not reach the declared accuracy #28

Open xiaoroulian opened 5 years ago

xiaoroulian commented 5 years ago

Hi, I ran the example for single db use the data you give in CASME2_Optical: https://drive.google.com/open?id=1fq_eHCLiUT9hP0npq6vkMYiO2Ka-39Mf CASME2_STRAIN: https://drive.google.com/open?id=1-l_CtP9awfMV6pXSrBIPRiIujQLjCv9H. The result is as follows： F1-score:0.2625012255303448 WAR:0.3617886178861789 UAR:0.2455069375069375

Since this result is too far from what you claim to be,I want to know if I have missed any important steps, or can I ask you to give me a trained model. I look forward to hearing from you.Thank you very much.

AIWeiJT commented 5 years ago

I have download links. But when I run the main.py, an error came up, " ImportError: No module named 'new_ram'". Can you help me? My email is weijintaochn@aliyun.com Thank you very much.

chineseqsc commented 5 years ago

How to set TIM length?Because I found that most micro-expression sequences are over 20 frames from onset to offset,Why did you set it 10? And which parameter in TIM correspnd to this length?Looking forward to your reply ~

XShunGao commented 4 years ago

Hi, do you find the problem about the result, can you tell me?And in the paper, i think f1 should be Macro F1-score

IcedDoggie commented 4 years ago

Ya if you changed to MF1, it would drop a little bit.

Sorry because I am not too sure about the problem, there are too much factors that could affect the problem.

Best Regards, Huai-Qian Khor -- Journey of thousand miles begins with a single step, Doing the best at this moment puts you in the best place for the next moment.

On Tue, 14 Jan 2020 at 21:17, XShunGao notifications@github.com wrote:

Hi, do you find the problem about the result, can you tell me?And in the paper, i think f1 should be Macro F1-score

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XShunGao commented 4 years ago

Thanks for your reply. I have questiones about this. When i use sklearn to calculate accuracy and F1, i find accuracy is equal to the micro-F1 in the multiple classes. And in the references 22, the method FDM, he use Macro-F1

IcedDoggie commented 4 years ago

Ya a bit of confusion. But the way I calculated it is summing all tp, fp and fn. U can find it in evaluationmatrix.py

On Wed, 15 Jan 2020, 17:11 XShunGao, notifications@github.com wrote:

Thanks for your reply. I have questiones about this. When i use sklearn to calculate accuracy and F1, i find accuracy is equal to the micro-F1 in the multiple classes. And in the references 22, the method FDM, he use Macro-F1

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XShunGao commented 4 years ago

Ya a bit of confusion. But the way I calculated it is summing all tp, fp and fn. U can find it in evaluationmatrix.py … On Wed, 15 Jan 2020, 17:11 XShunGao, @.***> wrote: Thanks for your reply. I have questiones about this. When i use sklearn to calculate accuracy and F1, i find accuracy is equal to the micro-F1 in the multiple classes. And in the references 22, the method FDM, he use Macro-F1 — You are receiving this because you commented. Reply to this email directly, view it on GitHub <#28?email_source=notifications&email_token=AA7ZNEEBH5EGBGQVK7P633LQ53HLRA5CNFSM4G7AZKQ2YY3PNVWWK3TUL52HS4DFVREXG43VMVBW63LNMVXHJKTDN5WW2ZLOORPWSZGOEI7SX2Q#issuecomment-574565354>, or unsubscribe https://github.com/notifications/unsubscribe-auth/AA7ZNECS7BAIEQZ3M2Y6HWLQ53HLRANCNFSM4G7AZKQQ .

Thanks. I will read the code. LOSO(Leave-One-Subject-Out) means leave a person's data to test, is right?

IcedDoggie commented 4 years ago

Yes. Best Regards, Huai-Qian Khor -- Journey of thousand miles begins with a single step, Doing the best at this moment puts you in the best place for the next moment.

On Wed, 15 Jan 2020 at 12:54, XShunGao notifications@github.com wrote:

Ya a bit of confusion. But the way I calculated it is summing all tp, fp and fn. U can find it in evaluationmatrix.py … <#m-4205260546320504722> On Wed, 15 Jan 2020, 17:11 XShunGao, @.***> wrote: Thanks for your reply. I have questiones about this. When i use sklearn to calculate accuracy and F1, i find accuracy is equal to the micro-F1 in the multiple classes. And in the references 22, the method FDM, he use Macro-F1 — You are receiving this because you commented. Reply to this email directly, view it on GitHub <#28 https://github.com/IcedDoggie/Micro-Expression-with-Deep-Learning/issues/28?email_source=notifications&email_token=AA7ZNEEBH5EGBGQVK7P633LQ53HLRA5CNFSM4G7AZKQ2YY3PNVWWK3TUL52HS4DFVREXG43VMVBW63LNMVXHJKTDN5WW2ZLOORPWSZGOEI7SX2Q#issuecomment-574565354>, or unsubscribe https://github.com/notifications/unsubscribe-auth/AA7ZNECS7BAIEQZ3M2Y6HWLQ53HLRANCNFSM4G7AZKQQ .

Thanks. I will read the code. LOSO(Leave-One-Subject-Out) means leave a person's data to test, is right?

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