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, accuracy achieves the same as paper.
- [NEW] The paper updated on 17 May, so I renew the codes for that, but there still are some bugs.
- [NEW] I remove the se before the global avg_pool (the paper may add it in error), and now the model size is close to paper.
- number of epochs: 150
- learning rate schedule: cosine learning rate, initial lr=0.05
- weight decay: 4e-5
- remove dropout
- batch size: 256
| 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 | 224 M | 5.48 M | 72.8% | - |
| Ours 0.75 | 148 M | 3.91 M | - | - |
| 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 | 63 M | 2.94 M | 67.4% | [google drive] |
| Ours 0.75 | 46 M | 2.38 M | - | - |
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_67.4.pth.tar')
net_small.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_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)