MobileNet V2体系结构和预训练模型的PyTorch实现。-Python开发

# A PyTorch implementation of MobileNetV2 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](https://arxiv.org/pdf/1801.04381). <u>**[NEW]** Add the code to automatically download the pre-trained weights.</u> ## Training Recipe Recently I have figured out a good training setting: 1. number of epochs: 150 2. learning rate schedule: cosine learning rate, initial lr=0.05 3. weight decay: 4e-5 4. remove dropout You should get >72% top-1 accuracy with this training recipe! ## Accuracy & Statistics Here is a comparison of statistics against the official TensorFlow [implementation](https://github.com/tensorflow/models/tree/master/research/slim/nets/mobilenet). | | 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](https://drive.google.com/open?id=1jlto6HRVD3ipNkAl1lNhDbkBp7HylaqR)] | ## Usage To use the pretrained model, run ```python from MobileNetV2 import mobilenet_v2 net = mobilenet_v2(pretrained=True) ``` ## Data Pre-processing I used the following code for data pre-processing on ImageNet: ```python 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) ```