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:

ResNet (resnet18, resnet34, resnet50, resnet101, resnet152)
DenseNet (densenet121, densenet169, densenet201, densenet161)
Inception v3 (inception_v3)
VGG (vgg11, vgg11_bn, vgg13, vgg13_bn, vgg16, vgg16_bn, vgg19, vgg19_bn)
SqueezeNet (squeezenet1_0, squeezenet1_1)
AlexNet (alexnet)

From Pretrained models for PyTorch package:

ResNeXt (resnext101_32x4d, resnext101_64x4d)
NASNet-A Large (nasnet_a_large: imagenet, imagenet+background)
NASNet-A Mobile (nasnet_a_mobile: imagenet)
Inception-ResNet v2 (inception_resnet_v2: imagenet, imagenet+background)
Dual Path Networks (dpn68, dpn68b, dpn92: imagenet, imagenet+5k, dpn98, dpn131, dpn107)
Inception v4 (inception_v4: imagenet, imagenet+background)
Xception (xception)
Squeeze-and-Excitation Networks (senet154, se_resnet50, se_resnet101, se_resnet152, se_resnext50_32x4d, se_resnext101_32x4d)
PNASNet-5-Large (pnasnet_5_large: imagenet, imagenet+background)
PolyNet (polynet)

From mobilenetV2 package:

Mobilenet V2 (mobilenet_v2)

From shufflenetV2 package:

Shufflenet V2 (shufflenet_v2)

From MnasNet package:

Mnasnet (MnasNet)

From mobilenetV3 package:

Mobilenet V3 (mobilenet_v3_large, mobilenet_v3_small)

From OctaveResnet package:

Octave Resnet (oct_resnet26, oct_resnet50, oct_resnet101, oct_resnet152, oct_resnet200)

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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