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image classification using pytorch-project-template, train a model of classification easily by modifying a json configuration

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image-classification-pytorch

This repo is designed for those who want to start their projects of image classification. It provides fast experiment setup and attempts to maximize the number of projects killed within the given time. It includes a few Convolutional Neural Network modules.You can build your own dnn easily.

Requirements

Python3 support only. Tested on CUDA9.0, cudnn7.

  • albumentations==0.1.1
  • easydict==1.8
  • imgaug==0.2.6
  • opencv-python==3.4.3.18
  • protobuf==3.6.1
  • scikit-image==0.14.0
  • tensorboardX==1.4
  • torch==0.4.1
  • torchvision==0.2.1

model

net inputsize
vggnet 224
alexnet 224
resnet 224
inceptionV3 299
inceptionV4 299
squeezenet 224
densenet 224
dpnnet 224
inception-resnet-v2 299
mobilenetV2 224
nasnet-a-large 331
nasnet-mobile 224
polynet 331
resnext 224
senet 224
squeezenet 224
pnasnet 331
shufflenetV2 224
mnasnet 224
mobilenetV3 224
oct-resnet 224/256
... ...

pre-trained model

you can download pretrain model with url in ($net-module.py)

From torchvision package:

From mobilenetV2 package:

From shufflenetV2 package:

From MnasNet package:

From mobilenetV3 package:

From OctaveResnet package:

usage

configuration

configure description
model_module_name eg: vgg_module
model_net_name net function name in module, eg:vgg16
gpu_id eg: single GPU: "0", multi-GPUs:"0,1,3,4,7"
async_loading make an asynchronous copy to the GPU
is_tensorboard if use tensorboard for visualization
evaluate_before_train evaluate accuracy before training
shuffle shuffle your training data
data_aug augment your training data
img_height input height
img_width input width
num_channels input channel
num_classes output number of classes
batch_size train batch size
dataloader_workers number of workers when loading data
learning_rate learning rate
learning_rate_decay learning rate decat rate
learning_rate_decay_epoch learning rate decay per n-epoch
train_mode eg: "fromscratch","finetune","update"
file_label_separator separator between data-name and label. eg:"----"
pretrained_path pretrain model path
pretrained_file pretrain model name. eg:"alexnet-owt-4df8aa71.pth"
pretrained_model_num_classes output number of classes when pretrain model trained. eg:1000 in imagenet
save_path model path when saving
save_name model name when saving
train_data_root_dir training data root dir
val_data_root_dir testing data root dir
train_data_file a txt filename which has training data and label list
val_data_file a txt filename which has testing data and label list

Training

1.make your training &. testing data and label list with txt file:

txt file with single label index eg:

apple.jpg----0
k.jpg----3
30.jpg----0
data/2.jpg----1
abc.jpg----1

2.configuration

3.train

python3 train.py

Inference

eg: trained by inception_resnet_v2, vgg/data/flowers/102:

python3 inference.py --image test.jpg --module inception_resnet_v2_module --net inception_resnet_v2 --model model.pth --size 299 --cls 102

tensorboardX

tensorboard --logdir='./logs/' runs

logdir is log dir in your project dir

References

1.https://github.com/pytorch
2.https://github.com/victoresque/pytorch-template
3.https://pytorch.org
5.https://github.com/yunjey/pytorch-tutorial
4.https://www.tensorflow.org
5.https://github.com/Cadene/pretrained-models.pytorch/tree/master/pretrainedmodels/models
6.https://github.com/ericsun99/MobileNet-V2-Pytorch
7.http://www.robots.ox.ac.uk/~vgg/data/flowers/102
8.https://github.com/ericsun99/Shufflenet-v2-Pytorch
9.https://github.com/billhhh/MnasNet-pytorch-pretrained
10.https://github.com/d-li14/octconv.pytorch
11.https://github.com/kuan-wang/pytorch-mobilenet-v3

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