image-classification-mxnet

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
mxboard 0.1.0
mxnet-cu90 1.3.0.post0

model

net	inputsize
vggnet	224
alexnet	224
resnet	224
inceptionV3	299
squeezenet	224
densenet	224
mobilenet	224
nasnet	331
resnext	224
senet	224
se_resnet	224
squeezenet	224
...	...

pre-trained model

you can download pretrain model with url in ($net-module.py)
And the accuracy of ImageNet pre-trained models is illustrated in the following URLs:

From mxnet-gluon-vision package:

ResNet (resnet18_v1, resnet34_v1, resnet50_v1, resnet101_v1, resnet152_v1, resnet18_v2, resnet34_v2, resnet50_v2, resnet101_v2, resnet152_v2)
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)
Mobilenet(mobilenet1_0, mobilenet_v2_1_0, mobilenet0_75, mobilenet_v2_0_75, mobilenet0_5, mobilenet_v2_0_5, mobilenet0_25, mobilenet_v2_0_25)

From Pretrained models for Mxnet-gluon package:

ResNeXt (resnext50_32x4d, resnext101_32x4d, resnext101_64x4d, se_resnext50_32x4d, se_resnext101_32x4d, se_resnext101_64x4d)
NASNet (nasnet_4_1056, nasnet_5_1538, nasnet_7_1920, nasnet_6_4032)
SENet (senet_52, senet_103, senet_154)
SE_ResNeXt (se_resnet18_v1, se_resnet34_v1, se_resnet50_v1, se_resnet101_v1, se_resnet152_v1, se_resnet18_v2, se_resnet34_v2, se_resnet50_v2, se_resnet101_v2, se_resnet152_v2)

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"
is_mxboard	if use tensorboard for visualization
evaluate_before_train	evaluate accuracy before training
shuffle	shuffle your training data
data_aug	augment your training data
model_auto_download	"True" for download pretrained model with gluonAPI in a automatic manner
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

python3 inference.py --image images/image_00002.jpg --module vgg --net vgg16 --model /hdd/datasets/flower_102/save_model/model.params --size 224 --cls 102

mxboard

tensorboard --logdir=./logs/

logdir is log dir in your project dir

experiment

There is integrated with the project using mxboard library which porved to be very useful as there is no official visualization library in mxnet. There is the learning curves for the flower-102 dataset experiment(top-1 acc: 87.74% for simple experiment).

top-5:
passion flower: 100.0%
king protea: 3.3845638097718123e-09%
barbeton daisy: 3.775002879735645e-10%
purple coneflower: 1.621601507587056e-10%
spring crocus: 1.3393154857724299e-10%

References

1.http://mxnet.incubator.apache.org/
2.https://github.com/dmlc/gluon-cv/tree/master/gluoncv/model_zoo
3.https://github.com/chinakook/Awesome-MXNet
4.https://github.com/awslabs/mxboard
5.http://www.robots.ox.ac.uk/~vgg/data/flowers/102
6.https://github.com/victoresque/pytorch-template

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