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MobileNet v3

MobileNetV3 is a convolutional neural network that is designed for mobile phone CPUs. The network design includes the use of a hard swish activation and squeeze-and-excitation modules in the MBConv blocks.

##
How do I use this model on an image?

To load a pretrained model:

    >>> import timm
    >>> model = timm.create_model('mobilenetv3_large_100', pretrained=True)
    >>> model.eval()

To load and preprocess the image:

    >>> import urllib
    >>> from PIL import Image
    >>> from timm.data import resolve_data_config
    >>> from timm.data.transforms_factory import create_transform

    >>> config = resolve_data_config({}, model=model)
    >>> transform = create_transform(**config)

    >>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
    >>> urllib.request.urlretrieve(url, filename)
    >>> img = Image.open(filename).convert('RGB')
    >>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension

To get the model predictions:

    >>> import torch
    >>> with torch.inference_mode():
    ...     out = model(tensor)
    >>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
    >>> print(probabilities.shape)
    >>> # prints: torch.Size([1000])

To get the top-5 predictions class names:

    >>> # Get imagenet class mappings
    >>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
    >>> urllib.request.urlretrieve(url, filename)
    >>> with open("imagenet_classes.txt", "r") as f:
    ...     categories = [s.strip() for s in f.readlines()]

    >>> # Print top categories per image
    >>> top5_prob, top5_catid = torch.topk(probabilities, 5)
    >>> for i in range(top5_prob.size(0)):
    ...     print(categories[top5_catid[i]], top5_prob[i].item())
    >>> # prints class names and probabilities like:
    >>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]

Replace the model name with the variant you want to use, e.g. `mobilenetv3_large_100`. You can find the IDs in the model summaries at the top of this page.

To extract image features with this model, follow the timm feature extraction examples, just change the name of the model you want to use.

##
How do I finetune this model?

You can finetune any of the pre-trained models just by changing the classifier (the last layer).

    >>> model = timm.create_model('mobilenetv3_large_100', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)

To finetune on your own dataset, you have to write a training loop or adapt timm’s training script to use your dataset.

##
How do I train this model?

You can follow the timm recipe scripts for training a new model afresh.

##
Citation

    @article{DBLP:journals/corr/abs-1905-02244,
      author    = {Andrew Howard and
                   Mark Sandler and
                   Grace Chu and
                   Liang{-}Chieh Chen and
                   Bo Chen and
                   Mingxing Tan and
                   Weijun Wang and
                   Yukun Zhu and
                   Ruoming Pang and
                   Vijay Vasudevan and
                   Quoc V. Le and
                   Hartwig Adam},
      title     = {Searching for MobileNetV3},
      journal   = {CoRR},
      volume    = {abs/1905.02244},
      year      = {2019},
      url       = {http://arxiv.org/abs/1905.02244},
      archivePrefix = {arXiv},
      eprint    = {1905.02244},
      timestamp = {Tue, 12 Jan 2021 15:30:06 +0100},
      biburl    = {https://dblp.org/rec/journals/corr/abs-1905-02244.bib},
      bibsource = {dblp computer science bibliography, https://dblp.org}
    }

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