This is a PyTorch implementation of MobileNetV2 architecture as described in the paper Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation.
[NEW] I fixed a difference in implementation compared to the official TensorFlow model. Please use the new model file and checkpoint!
I tried to train the model with RMSprop from scratch as described in the paper, but it does not seem to work. I currently train the model with SGD and keeping other hyper-parameters the same (except that I use batch size 256).
Here is a comparison of statistics against the official TensorFlow implementation.
| FLOPs | Parameters | Top1-acc | Pretrained Model | |
|---|---|---|---|---|
| Official TF | 300 M | 3.47 M | 71.8% | - |
| Ours | 300.775 M | 3.471 M | 71.8% | [google drive] |
To use the pretrained model, run
from MobileNetV2 import MobileNetV2
net = MobileNetV2(n_class=1000)
state_dict = torch.load('mobilenetv2.pth.tar') # add map_location='cpu' if no gpu
net.load_state_dict(state_dict)I used the following code for data pre-processing on ImageNet:
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
input_size = 224
train_dataset = datasets.ImageFolder(
traindir,
transforms.Compose([
transforms.RandomResizedCrop(input_size, scale=(0.2, 1.0)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
]))
train_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=batch_size, shuffle=True,
num_workers=n_worker, pin_memory=True)
val_loader = torch.utils.data.DataLoader(
datasets.ImageFolder(valdir, transforms.Compose([
transforms.Resize(int(input_size/0.875)),
transforms.CenterCrop(input_size),
transforms.ToTensor(),
normalize,
])),
batch_size=batch_size, shuffle=False,
num_workers=n_worker, pin_memory=True)