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pytorch implementation of network-in-network model on cifar10

Python 100.00%

pytorch-nin-cifar10's Introduction

pytorch-nin-cifar10

pytorch implementation of network-in-network model on cifar10. All settings are base on the network-in-network model in Caffe model zoo.

Instructions

$ git clone https://github.com/jiecaoyu/pytorch-nin-cifar10.git
$ cd pytorch-nin-cifar10
$ mkdir data

Then download the data from this link and uncompress it into the ./data/ directory. Now you can train the model by running

$ python original.py

Accuracy

By tweaking hyper-parameters, the model can reach the accuracy of 89.64%, which is better than other available Torch/PyTorch implementations.

License

The data used to train this model comes from http://www.cs.toronto.edu/~kriz/cifar.html Please follow the license there if used.

pytorch-nin-cifar10's People

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lxgychen tungk niluanwudidadi wtakase css1995 hamzajaved05 hisangke xiang0716 noticeable skyloveshow sanaamruth eryoyo

pytorch-nin-cifar10's Issues

cifar10 dataset

Is it possible to know in what the cifar10 version reported in the repository differs from the original one?

where is mlp conv block?

nin in papers, they replace conv into mlp conv
it is not nin structure i think...

hyperparameters for .89 accuracy?

Pretrained model

Hi! Do you have a pretrained model with the given accuracies on cifar10? Thanks in advance

Test accuracy stays the same

Hello, I ran original.py according to the instuctions, but the test accuracy & loss stays the same when I ran it through 10 epochs. Do you know what could be the reason?

Train Epoch: 1 [0/50000 (0%)] Loss: 2.303388 LR: 0.1
Train Epoch: 1 [12800/50000 (26%)] Loss: 2.289754 LR: 0.1
Train Epoch: 1 [25600/50000 (51%)] Loss: 2.233992 LR: 0.1
Train Epoch: 1 [38400/50000 (77%)] Loss: 2.302584 LR: 0.1

Test set: Average loss: 2.9473, Accuracy: 1000/10000 (10.00%)

Train Epoch: 2 [0/50000 (0%)] Loss: 2.302585 LR: 0.1
Train Epoch: 2 [12800/50000 (26%)] Loss: 2.302585 LR: 0.1
Train Epoch: 2 [25600/50000 (51%)] Loss: 2.302585 LR: 0.1
Train Epoch: 2 [38400/50000 (77%)] Loss: 2.302585 LR: 0.1

Test set: Average loss: 2.9473, Accuracy: 1000/10000 (10.00%)

Train Epoch: 3 [0/50000 (0%)] Loss: 2.302585 LR: 0.1
Train Epoch: 3 [12800/50000 (26%)] Loss: 2.302585 LR: 0.1
Train Epoch: 3 [25600/50000 (51%)] Loss: 2.302585 LR: 0.1
Train Epoch: 3 [38400/50000 (77%)] Loss: 2.302585 LR: 0.1

Test set: Average loss: 2.9473, Accuracy: 1000/10000 (10.00%)

Train Epoch: 4 [0/50000 (0%)] Loss: 2.302585 LR: 0.1
Train Epoch: 4 [12800/50000 (26%)] Loss: 2.302585 LR: 0.1
Train Epoch: 4 [25600/50000 (51%)] Loss: 2.302585 LR: 0.1
Train Epoch: 4 [38400/50000 (77%)] Loss: 2.302585 LR: 0.1

what's the difference between your dataset and the offical

can you explain the details, do you use Normalize?

Miss spelling

You used Classifer while the correct one is Classifier, this looks weird

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