A PyTorch implementation of MobileNetV3
This is a PyTorch implementation of MobileNetV3 architecture as described in the paper Searching for MobileNetV3.
Some details may be different from the original paper, welcome to discuss and help me figure it out.
[NEW] The pretrained model of small version mobilenet-v3 is online.
Training & Accuracy
In progress ...
MobileNetV3 large
| Madds | Parameters | Top1-acc | Pretrained Model | |
|---|---|---|---|---|
| Offical 1.0 | 219 M | 5.4 M | 75.2% | - |
| Offical 0.75 | 155 M | 4 M | 73.3% | - |
| Ours 1.0 | - M | 5.08 M | - | - |
| Ours 0.75 | - M | 3.69 M | - | - |
MobileNetV3 small
| Madds | Parameters | Top1-acc | Pretrained Model | |
|---|---|---|---|---|
| Offical 1.0 | 66 M | 2.9 M | 67.4% | - |
| Offical 0.75 | 44 M | 2.4 M | 65.4% | - |
| Ours 1.0 | 68 M | 3.11 M | 67.218% | [google drive] |
| Ours 0.75 | - M | 2.47 M | - | - |
Usage
Pretrained models are still training ...
# pytorch 1.0.1
# large
net_large = mobilenetv3(mode='large')
# small
net_small = mobilenetv3(mode='small')
state_dict = torch.load('mobilenetv3_small_67.218.pth.tar')
net_small.load_state_dict(state_dict)Data Pre-processing
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_loader = torch.utils.data.DataLoader(
datasets.ImageFolder(
traindir, transforms.Compose([
transforms.RandomResizedCrop(input_size),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
])),
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)
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