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
| 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 |
| ... | ... |
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)
| 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 |
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
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
tensorboard --logdir='./logs/' runs
logdir is log dir in your project dir