- This repository is for kaggle
Dogs vs. Catsmatch, but you can utilize this code to learn how to usepytorch. - For network, I has estabilished the structure containing the introduction of pre-trained models like
VGG,Inceptionv3andResNet. - For optimizer, only
Adamis illustrated in my repository.
- Python 3.6
- Pytorch 0.4.0
- torchvision 0.2.0
- tensorboardX 1.5
${project_dir}/datasets
dogs_vs_cats
train.zip
test1.zip
After downloading the datasets from Kaggle website, you need to extract these two zips.(Actually, I just extract train.zip)
${project_dir}/datasets
dogs-vs-cats
train.zip
test1.zip
Dataset # Extracted from train.zip
test1 # Extracted from test1.zip
- In fact, the filenames of train and test datasets is in different naming conventions.
- For train one, the filename name is in cat.x.jpg or dog.x.jpg. However, x.jpg is used in test dataset.
- To do the classification task easily, I just use the train dataset(Dataset). So if you want to build a new test/val dataset. You need to run
redistribution_image.pyto split the train dataset into a new one and an extra test dataset.
${project_dir}/datasets
dogs-vs-cats
train.zip
test1.zip
train # Separated from 'train' directory by run `redistribution_image.py`.
val # Separated from 'train' directory by run `redistribution_image.py`.
Dataset # Extracted from train.zip
test1 # Extracted from test1.zip
- Before training, you need to modify the directories in
demo.py - Run
demo.pyto start the training process. The follow directories will be created automatically.
${project_dir}
checkpoint
inceptionv3
ckpt.t7
resnet50
ckpt.t7
vgg
ckpt.t7
log(tensorboard dirctory)
inceptionv3
resnet50
vgg
model
inceptionv3
net_ 1.pth
net_ 2.pth
...
resnet50
net_ 1.pth
net_ 2.pth
...
vgg
net_ 1.pth
net_ 2.pth
...
record
inceptionv3
acc.txt
log.txt
resnet50
acc.txt
log.txt
vgg
acc.txt
log.txt
...
- You can walk into
log\$(use_model)directory to monitor the loss. Runtensorboard --logdir .then open the browser.
| Network | Test Accuracy(%) | batch_size |
|---|---|---|
| VGG19 | 96.00 | 4 |
| ResNet50 | 96.00 | 32 |
| InceptionV3 | 76.00 | 32 |
- I has just trained the models for 1 epochs by 'Adam'.
If the train or test accuracy is low, you can modify the optimizer code to
optimizer = torch.optim.Adam(use_model.parameters())
to let all the parameters for training.
Besides, you need to comment the following code:
for parma in use_model.feature.parameters():
parma.requires_grad = False
for index, parma in enumerate(use_model.classifier.parameters()):
if index == 6:
parma.requires_grad = True
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