Testing Accuracy is less than half of Trainings Accuracy, and manual testing result is way off











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Firstly I have to admit that I am total beginner of PyTorch and CNN image classification.



I am making an app to classify cat breeds.



The image sets I gathered have around 300-500 per breed, with a total of 62 breeds, plus one other set which represents non-cats which contains 600 samples. I have split the samples into training and testing respectively at 4:1 ratio.



The training results are quite disappointing. The training accuracy can reach as much as 90% but the testing results are only 39%.



Here are the hyperparameters:



LR is 0.1, momentum is 0.1, and batch_size is 128, wideresnet is using 40 layers of widen factor of 10.



Please see the source code at:



https://github.com/silver-xu/wideresnet-trial



I have searched all over the internet and pretty much 90% of the articles are about pre-compiled datasets like cifar or MNIST. As a result lots of the codes which I found only is optimised for one type of dataset only.



Thanks for all the help! Critics are also Welcome!



Here is training output for Epoch 5:



Epoch: [5][0/170]       Time 0.237 (0.237)      Loss 3.3054 (3.3054)    Prec@1 13.281 (13.281)

Epoch: [5][10/170]      Time 0.229 (0.228)      Loss 3.2665 (3.3118)    Prec@1 14.844 (13.920)

Epoch: [5][20/170]      Time 0.227 (0.227)      Loss 3.0962 (3.2856)    Prec@1 17.969 (14.695)

Epoch: [5][30/170]      Time 0.228 (0.227)      Loss 3.3670 (3.2853)    Prec@1 10.938 (14.844)

Epoch: [5][40/170]      Time 0.229 (0.227)      Loss 3.3259 (3.2917)    Prec@1 15.625 (15.282)

Epoch: [5][50/170]      Time 0.228 (0.227)      Loss 3.2016 (3.2931)    Prec@1 14.844 (14.859)

Epoch: [5][60/170]      Time 0.227 (0.227)      Loss 3.3739 (3.3071)    Prec@1 11.719 (14.677)

Epoch: [5][70/170]      Time 0.227 (0.227)      Loss 3.4417 (3.3042)    Prec@1 15.625 (14.833)

Epoch: [5][80/170]      Time 0.226 (0.227)      Loss 3.2507 (3.2996)    Prec@1 10.938 (14.911)

Epoch: [5][90/170]      Time 0.224 (0.227)      Loss 3.2627 (3.2978)    Prec@1 14.844 (15.093)

Epoch: [5][100/170]     Time 0.226 (0.227)      Loss 3.3668 (3.2946)    Prec@1 14.062 (15.060)

Epoch: [5][110/170]     Time 0.225 (0.227)      Loss 3.2839 (3.2915)    Prec@1 10.156 (14.921)

Epoch: [5][120/170]     Time 0.227 (0.227)      Loss 3.3308 (3.2906)    Prec@1 11.719 (14.837)

Epoch: [5][130/170]     Time 0.224 (0.227)      Loss 3.1656 (3.2885)    Prec@1 21.875 (14.909)

Epoch: [5][140/170]     Time 0.226 (0.227)      Loss 3.2521 (3.2851)    Prec@1 20.312 (14.966)

Epoch: [5][150/170]     Time 0.227 (0.227)      Loss 3.1261 (3.2825)    Prec@1 14.844 (14.989)

Epoch: [5][160/170]     Time 0.227 (0.227)      Loss 3.4400 (3.2802)    Prec@1 10.938 (15.018)

Test: [0/43]    Time 0.262 (0.262)      Loss 3.6978 (3.6978)    Prec@1 8.594 (8.594)

Test: [10/43]   Time 0.074 (0.091)      Loss 3.3584 (3.3736)    Prec@1 17.188 (13.139)

Test: [20/43]   Time 0.074 (0.083)      Loss 3.3834 (3.4058)    Prec@1 12.500 (12.537)

Test: [30/43]   Time 0.074 (0.080)      Loss 3.4457 (3.3994)    Prec@1 14.844 (12.802)

Test: [40/43]   Time 0.074 (0.079)      Loss 3.2851 (3.3946)    Prec@1 16.406 (13.281)

 * Prec@1 13.130





python pytorch







share|improve this question
























  • Have you trained with a validation set? How does the validation loss compare to the training loss?
    – Charles Landau
    Nov 19 at 0:04










  • Great! It sounds like you may be overfitting and I would look for early divergence in validation loss (e.g. in epoch < 5) as another indicator of overfitting.
    – Charles Landau
    Nov 19 at 0:21










  • Here you go. Greatly appreciated for your help.
    – Silver Xu
    Nov 19 at 0:33






  • 1




    You don't appear to be training with a validation set, see this discussion for some basic approaches to train, validation, test patterns in pytorch github.com/pytorch/pytorch/issues/1106
    – Charles Landau
    Nov 19 at 0:57










  • @Charles Landau I've done what you commented above. The result improved to around 60%. Is there anything I can improve further?
    – Silver Xu
    Nov 19 at 10:52















up vote
1
down vote

favorite












Firstly I have to admit that I am total beginner of PyTorch and CNN image classification.



I am making an app to classify cat breeds.



The image sets I gathered have around 300-500 per breed, with a total of 62 breeds, plus one other set which represents non-cats which contains 600 samples. I have split the samples into training and testing respectively at 4:1 ratio.



The training results are quite disappointing. The training accuracy can reach as much as 90% but the testing results are only 39%.



Here are the hyperparameters:



LR is 0.1, momentum is 0.1, and batch_size is 128, wideresnet is using 40 layers of widen factor of 10.



Please see the source code at:



https://github.com/silver-xu/wideresnet-trial



I have searched all over the internet and pretty much 90% of the articles are about pre-compiled datasets like cifar or MNIST. As a result lots of the codes which I found only is optimised for one type of dataset only.



Thanks for all the help! Critics are also Welcome!



Here is training output for Epoch 5:



Epoch: [5][0/170]       Time 0.237 (0.237)      Loss 3.3054 (3.3054)    Prec@1 13.281 (13.281)

Epoch: [5][10/170]      Time 0.229 (0.228)      Loss 3.2665 (3.3118)    Prec@1 14.844 (13.920)

Epoch: [5][20/170]      Time 0.227 (0.227)      Loss 3.0962 (3.2856)    Prec@1 17.969 (14.695)

Epoch: [5][30/170]      Time 0.228 (0.227)      Loss 3.3670 (3.2853)    Prec@1 10.938 (14.844)

Epoch: [5][40/170]      Time 0.229 (0.227)      Loss 3.3259 (3.2917)    Prec@1 15.625 (15.282)

Epoch: [5][50/170]      Time 0.228 (0.227)      Loss 3.2016 (3.2931)    Prec@1 14.844 (14.859)

Epoch: [5][60/170]      Time 0.227 (0.227)      Loss 3.3739 (3.3071)    Prec@1 11.719 (14.677)

Epoch: [5][70/170]      Time 0.227 (0.227)      Loss 3.4417 (3.3042)    Prec@1 15.625 (14.833)

Epoch: [5][80/170]      Time 0.226 (0.227)      Loss 3.2507 (3.2996)    Prec@1 10.938 (14.911)

Epoch: [5][90/170]      Time 0.224 (0.227)      Loss 3.2627 (3.2978)    Prec@1 14.844 (15.093)

Epoch: [5][100/170]     Time 0.226 (0.227)      Loss 3.3668 (3.2946)    Prec@1 14.062 (15.060)

Epoch: [5][110/170]     Time 0.225 (0.227)      Loss 3.2839 (3.2915)    Prec@1 10.156 (14.921)

Epoch: [5][120/170]     Time 0.227 (0.227)      Loss 3.3308 (3.2906)    Prec@1 11.719 (14.837)

Epoch: [5][130/170]     Time 0.224 (0.227)      Loss 3.1656 (3.2885)    Prec@1 21.875 (14.909)

Epoch: [5][140/170]     Time 0.226 (0.227)      Loss 3.2521 (3.2851)    Prec@1 20.312 (14.966)

Epoch: [5][150/170]     Time 0.227 (0.227)      Loss 3.1261 (3.2825)    Prec@1 14.844 (14.989)

Epoch: [5][160/170]     Time 0.227 (0.227)      Loss 3.4400 (3.2802)    Prec@1 10.938 (15.018)

Test: [0/43]    Time 0.262 (0.262)      Loss 3.6978 (3.6978)    Prec@1 8.594 (8.594)

Test: [10/43]   Time 0.074 (0.091)      Loss 3.3584 (3.3736)    Prec@1 17.188 (13.139)

Test: [20/43]   Time 0.074 (0.083)      Loss 3.3834 (3.4058)    Prec@1 12.500 (12.537)

Test: [30/43]   Time 0.074 (0.080)      Loss 3.4457 (3.3994)    Prec@1 14.844 (12.802)

Test: [40/43]   Time 0.074 (0.079)      Loss 3.2851 (3.3946)    Prec@1 16.406 (13.281)

 * Prec@1 13.130





python pytorch







share|improve this question
























  • Have you trained with a validation set? How does the validation loss compare to the training loss?
    – Charles Landau
    Nov 19 at 0:04










  • Great! It sounds like you may be overfitting and I would look for early divergence in validation loss (e.g. in epoch < 5) as another indicator of overfitting.
    – Charles Landau
    Nov 19 at 0:21










  • Here you go. Greatly appreciated for your help.
    – Silver Xu
    Nov 19 at 0:33






  • 1




    You don't appear to be training with a validation set, see this discussion for some basic approaches to train, validation, test patterns in pytorch github.com/pytorch/pytorch/issues/1106
    – Charles Landau
    Nov 19 at 0:57










  • @Charles Landau I've done what you commented above. The result improved to around 60%. Is there anything I can improve further?
    – Silver Xu
    Nov 19 at 10:52













up vote
1
down vote

favorite









up vote
1
down vote

favorite











Firstly I have to admit that I am total beginner of PyTorch and CNN image classification.



I am making an app to classify cat breeds.



The image sets I gathered have around 300-500 per breed, with a total of 62 breeds, plus one other set which represents non-cats which contains 600 samples. I have split the samples into training and testing respectively at 4:1 ratio.



The training results are quite disappointing. The training accuracy can reach as much as 90% but the testing results are only 39%.



Here are the hyperparameters:



LR is 0.1, momentum is 0.1, and batch_size is 128, wideresnet is using 40 layers of widen factor of 10.



Please see the source code at:



https://github.com/silver-xu/wideresnet-trial



I have searched all over the internet and pretty much 90% of the articles are about pre-compiled datasets like cifar or MNIST. As a result lots of the codes which I found only is optimised for one type of dataset only.



Thanks for all the help! Critics are also Welcome!



Here is training output for Epoch 5:



Epoch: [5][0/170]       Time 0.237 (0.237)      Loss 3.3054 (3.3054)    Prec@1 13.281 (13.281)

Epoch: [5][10/170]      Time 0.229 (0.228)      Loss 3.2665 (3.3118)    Prec@1 14.844 (13.920)

Epoch: [5][20/170]      Time 0.227 (0.227)      Loss 3.0962 (3.2856)    Prec@1 17.969 (14.695)

Epoch: [5][30/170]      Time 0.228 (0.227)      Loss 3.3670 (3.2853)    Prec@1 10.938 (14.844)

Epoch: [5][40/170]      Time 0.229 (0.227)      Loss 3.3259 (3.2917)    Prec@1 15.625 (15.282)

Epoch: [5][50/170]      Time 0.228 (0.227)      Loss 3.2016 (3.2931)    Prec@1 14.844 (14.859)

Epoch: [5][60/170]      Time 0.227 (0.227)      Loss 3.3739 (3.3071)    Prec@1 11.719 (14.677)

Epoch: [5][70/170]      Time 0.227 (0.227)      Loss 3.4417 (3.3042)    Prec@1 15.625 (14.833)

Epoch: [5][80/170]      Time 0.226 (0.227)      Loss 3.2507 (3.2996)    Prec@1 10.938 (14.911)

Epoch: [5][90/170]      Time 0.224 (0.227)      Loss 3.2627 (3.2978)    Prec@1 14.844 (15.093)

Epoch: [5][100/170]     Time 0.226 (0.227)      Loss 3.3668 (3.2946)    Prec@1 14.062 (15.060)

Epoch: [5][110/170]     Time 0.225 (0.227)      Loss 3.2839 (3.2915)    Prec@1 10.156 (14.921)

Epoch: [5][120/170]     Time 0.227 (0.227)      Loss 3.3308 (3.2906)    Prec@1 11.719 (14.837)

Epoch: [5][130/170]     Time 0.224 (0.227)      Loss 3.1656 (3.2885)    Prec@1 21.875 (14.909)

Epoch: [5][140/170]     Time 0.226 (0.227)      Loss 3.2521 (3.2851)    Prec@1 20.312 (14.966)

Epoch: [5][150/170]     Time 0.227 (0.227)      Loss 3.1261 (3.2825)    Prec@1 14.844 (14.989)

Epoch: [5][160/170]     Time 0.227 (0.227)      Loss 3.4400 (3.2802)    Prec@1 10.938 (15.018)

Test: [0/43]    Time 0.262 (0.262)      Loss 3.6978 (3.6978)    Prec@1 8.594 (8.594)

Test: [10/43]   Time 0.074 (0.091)      Loss 3.3584 (3.3736)    Prec@1 17.188 (13.139)

Test: [20/43]   Time 0.074 (0.083)      Loss 3.3834 (3.4058)    Prec@1 12.500 (12.537)

Test: [30/43]   Time 0.074 (0.080)      Loss 3.4457 (3.3994)    Prec@1 14.844 (12.802)

Test: [40/43]   Time 0.074 (0.079)      Loss 3.2851 (3.3946)    Prec@1 16.406 (13.281)

 * Prec@1 13.130





python pytorch







share|improve this question















Firstly I have to admit that I am total beginner of PyTorch and CNN image classification.



I am making an app to classify cat breeds.



The image sets I gathered have around 300-500 per breed, with a total of 62 breeds, plus one other set which represents non-cats which contains 600 samples. I have split the samples into training and testing respectively at 4:1 ratio.



The training results are quite disappointing. The training accuracy can reach as much as 90% but the testing results are only 39%.



Here are the hyperparameters:



LR is 0.1, momentum is 0.1, and batch_size is 128, wideresnet is using 40 layers of widen factor of 10.



Please see the source code at:



https://github.com/silver-xu/wideresnet-trial



I have searched all over the internet and pretty much 90% of the articles are about pre-compiled datasets like cifar or MNIST. As a result lots of the codes which I found only is optimised for one type of dataset only.



Thanks for all the help! Critics are also Welcome!



Here is training output for Epoch 5:



Epoch: [5][0/170]       Time 0.237 (0.237)      Loss 3.3054 (3.3054)    Prec@1 13.281 (13.281)

Epoch: [5][10/170]      Time 0.229 (0.228)      Loss 3.2665 (3.3118)    Prec@1 14.844 (13.920)

Epoch: [5][20/170]      Time 0.227 (0.227)      Loss 3.0962 (3.2856)    Prec@1 17.969 (14.695)

Epoch: [5][30/170]      Time 0.228 (0.227)      Loss 3.3670 (3.2853)    Prec@1 10.938 (14.844)

Epoch: [5][40/170]      Time 0.229 (0.227)      Loss 3.3259 (3.2917)    Prec@1 15.625 (15.282)

Epoch: [5][50/170]      Time 0.228 (0.227)      Loss 3.2016 (3.2931)    Prec@1 14.844 (14.859)

Epoch: [5][60/170]      Time 0.227 (0.227)      Loss 3.3739 (3.3071)    Prec@1 11.719 (14.677)

Epoch: [5][70/170]      Time 0.227 (0.227)      Loss 3.4417 (3.3042)    Prec@1 15.625 (14.833)

Epoch: [5][80/170]      Time 0.226 (0.227)      Loss 3.2507 (3.2996)    Prec@1 10.938 (14.911)

Epoch: [5][90/170]      Time 0.224 (0.227)      Loss 3.2627 (3.2978)    Prec@1 14.844 (15.093)

Epoch: [5][100/170]     Time 0.226 (0.227)      Loss 3.3668 (3.2946)    Prec@1 14.062 (15.060)

Epoch: [5][110/170]     Time 0.225 (0.227)      Loss 3.2839 (3.2915)    Prec@1 10.156 (14.921)

Epoch: [5][120/170]     Time 0.227 (0.227)      Loss 3.3308 (3.2906)    Prec@1 11.719 (14.837)

Epoch: [5][130/170]     Time 0.224 (0.227)      Loss 3.1656 (3.2885)    Prec@1 21.875 (14.909)

Epoch: [5][140/170]     Time 0.226 (0.227)      Loss 3.2521 (3.2851)    Prec@1 20.312 (14.966)

Epoch: [5][150/170]     Time 0.227 (0.227)      Loss 3.1261 (3.2825)    Prec@1 14.844 (14.989)

Epoch: [5][160/170]     Time 0.227 (0.227)      Loss 3.4400 (3.2802)    Prec@1 10.938 (15.018)

Test: [0/43]    Time 0.262 (0.262)      Loss 3.6978 (3.6978)    Prec@1 8.594 (8.594)

Test: [10/43]   Time 0.074 (0.091)      Loss 3.3584 (3.3736)    Prec@1 17.188 (13.139)

Test: [20/43]   Time 0.074 (0.083)      Loss 3.3834 (3.4058)    Prec@1 12.500 (12.537)

Test: [30/43]   Time 0.074 (0.080)      Loss 3.4457 (3.3994)    Prec@1 14.844 (12.802)

Test: [40/43]   Time 0.074 (0.079)      Loss 3.2851 (3.3946)    Prec@1 16.406 (13.281)

 * Prec@1 13.130





python pytorch




python pytorch






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited Nov 19 at 0:56

























asked Nov 18 at 23:24









Silver Xu

62




62












  • Have you trained with a validation set? How does the validation loss compare to the training loss?
    – Charles Landau
    Nov 19 at 0:04










  • Great! It sounds like you may be overfitting and I would look for early divergence in validation loss (e.g. in epoch < 5) as another indicator of overfitting.
    – Charles Landau
    Nov 19 at 0:21










  • Here you go. Greatly appreciated for your help.
    – Silver Xu
    Nov 19 at 0:33






  • 1




    You don't appear to be training with a validation set, see this discussion for some basic approaches to train, validation, test patterns in pytorch github.com/pytorch/pytorch/issues/1106
    – Charles Landau
    Nov 19 at 0:57










  • @Charles Landau I've done what you commented above. The result improved to around 60%. Is there anything I can improve further?
    – Silver Xu
    Nov 19 at 10:52


















  • Have you trained with a validation set? How does the validation loss compare to the training loss?
    – Charles Landau
    Nov 19 at 0:04










  • Great! It sounds like you may be overfitting and I would look for early divergence in validation loss (e.g. in epoch < 5) as another indicator of overfitting.
    – Charles Landau
    Nov 19 at 0:21










  • Here you go. Greatly appreciated for your help.
    – Silver Xu
    Nov 19 at 0:33






  • 1




    You don't appear to be training with a validation set, see this discussion for some basic approaches to train, validation, test patterns in pytorch github.com/pytorch/pytorch/issues/1106
    – Charles Landau
    Nov 19 at 0:57










  • @Charles Landau I've done what you commented above. The result improved to around 60%. Is there anything I can improve further?
    – Silver Xu
    Nov 19 at 10:52
















Have you trained with a validation set? How does the validation loss compare to the training loss?
– Charles Landau
Nov 19 at 0:04




Have you trained with a validation set? How does the validation loss compare to the training loss?
– Charles Landau
Nov 19 at 0:04












Great! It sounds like you may be overfitting and I would look for early divergence in validation loss (e.g. in epoch < 5) as another indicator of overfitting.
– Charles Landau
Nov 19 at 0:21




Great! It sounds like you may be overfitting and I would look for early divergence in validation loss (e.g. in epoch < 5) as another indicator of overfitting.
– Charles Landau
Nov 19 at 0:21












Here you go. Greatly appreciated for your help.
– Silver Xu
Nov 19 at 0:33




Here you go. Greatly appreciated for your help.
– Silver Xu
Nov 19 at 0:33




1




1




You don't appear to be training with a validation set, see this discussion for some basic approaches to train, validation, test patterns in pytorch github.com/pytorch/pytorch/issues/1106
– Charles Landau
Nov 19 at 0:57




You don't appear to be training with a validation set, see this discussion for some basic approaches to train, validation, test patterns in pytorch github.com/pytorch/pytorch/issues/1106
– Charles Landau
Nov 19 at 0:57












@Charles Landau I've done what you commented above. The result improved to around 60%. Is there anything I can improve further?
– Silver Xu
Nov 19 at 10:52




@Charles Landau I've done what you commented above. The result improved to around 60%. Is there anything I can improve further?
– Silver Xu
Nov 19 at 10:52

















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Alcedinidae

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Alcedinidae Martin-pêcheur à dos bleu ( Alcedo azurea ) Classification (COI) Règne Animalia Embranchement Chordata Sous-embr. Vertebrata Classe Aves Ordre Coraciiformes Famille Alcedinidae Rafinesque, 1815 Les Alcédinidés ou Alcedinidae forment une famille d'oiseaux plus connus sous les noms de martins-pêcheurs et martins-chasseurs. Sommaire 1 Description 2 Répartition et habitat 3 Systématique 3.1 Liste alphabétique des genres 3.2 Liste des espèces 4 Chez les Anciens : l'alcyon 4.1 Étymologie (Littré) 4.2 Oiseau « fabuleux » ? 5 Notes 6 Articles connexes 7 Liens externes 7.1 Bibliographie Description | Ce sont des oiseaux compacts de taille petite à moyenne (10 à 46 cm), au bec droit et long, en forme de poignard. Leurs pattes sont courtes, et ils portent un plumage aux couleurs vives. Répartition et habitat | Cosmopolites, ils fréquentent surtout ...
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Origin of the phrase “under your belt”?

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.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty{ margin-bottom:0; } 17 1 Today, I found myself discussing what students should have "under their belts" during a lecture, and I wondered to myself if there was some inappropriate undertone here that I might be unaware of. Question : What is the origin of the phrase "under your belt"? A google search revealed some webpages (e.g. [1]) that assert its origin is related to consumption of food -- once a meal has been eaten, it's under your belt (which is a relief). However, with such websites, I have no way to determine fact from "random guy on the internet making stuff up". etymology idioms ...
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