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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RAC Tourist Trophy

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Pour les articles homonymes, voir Tourist Trophy. Le RAC Tourist Trophy est une course automobile organisée par le Royal Automobile Club d'Angleterre. Créée en 1905, cette course a fait partie des épreuves comptant pour le championnat du monde des voitures de sport entre 1953 et 1955, en 1958 et 1959, et entre 1962 et 1965. Après une interruption de 17 ans (l'épreuve s'arrête après 1988), le RAC Tourist Trophy reprend en 2005 sous l'égide de la FIA GT. Palmarès | John Napier sur Arrol-Johnston, vainqueur du premier RAC Tourist Trophy, en 1905. L'honorable C.S. Rolls, vainqueur du RAC Tourist Trophy 1906. Ernest Curtis, sur Rover 20 hp, vainqueur du RAC Tourist Trophy 1907. William Watson, vainqueur du RAC Tourist Trophy 1908. Kenelm Lee Guinness, vainqueur du RAC Tourist Trophy 1914 sur Sunbeam. Départ du RAC Tourist Trophy 1928. Louis Gérard, vainqueur du RAC Tourist Trophy 1938 sur Delage. Année Circuit Vainqueur
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