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277 lines
8.5 KiB
277 lines
8.5 KiB
# MobileNet v2
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**MobileNetV2** is a convolutional neural network architecture that seeks to perform well on mobile devices. It is based on an [inverted residual structure](https://paperswithcode.com/method/inverted-residual-block) where the residual connections are between the bottleneck layers. The intermediate expansion layer uses lightweight depthwise convolutions to filter features as a source of non-linearity. As a whole, the architecture of MobileNetV2 contains the initial fully convolution layer with 32 filters, followed by 19 residual bottleneck layers.
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## How do I use this model on an image?
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To load a pretrained model:
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```py
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>>> import timm
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>>> model = timm.create_model('mobilenetv2_100', pretrained=True)
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>>> model.eval()
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```
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To load and preprocess the image:
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```py
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>>> import urllib
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>>> from PIL import Image
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>>> from timm.data import resolve_data_config
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>>> from timm.data.transforms_factory import create_transform
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>>> config = resolve_data_config({}, model=model)
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>>> transform = create_transform(**config)
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>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
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>>> urllib.request.urlretrieve(url, filename)
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>>> img = Image.open(filename).convert('RGB')
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>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension
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```
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To get the model predictions:
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```py
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>>> import torch
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>>> with torch.no_grad():
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... out = model(tensor)
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>>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
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>>> print(probabilities.shape)
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>>> # prints: torch.Size([1000])
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```
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To get the top-5 predictions class names:
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```py
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>>> # Get imagenet class mappings
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>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
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>>> urllib.request.urlretrieve(url, filename)
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>>> with open("imagenet_classes.txt", "r") as f:
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... categories = [s.strip() for s in f.readlines()]
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>>> # Print top categories per image
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>>> top5_prob, top5_catid = torch.topk(probabilities, 5)
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>>> for i in range(top5_prob.size(0)):
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... print(categories[top5_catid[i]], top5_prob[i].item())
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>>> # prints class names and probabilities like:
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>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
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```
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Replace the model name with the variant you want to use, e.g. `mobilenetv2_100`. You can find the IDs in the model summaries at the top of this page.
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To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use.
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## How do I finetune this model?
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You can finetune any of the pre-trained models just by changing the classifier (the last layer).
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```py
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>>> model = timm.create_model('mobilenetv2_100', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
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```
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To finetune on your own dataset, you have to write a training loop or adapt [timm's training
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script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
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## How do I train this model?
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You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
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## Citation
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```BibTeX
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@article{DBLP:journals/corr/abs-1801-04381,
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author = {Mark Sandler and
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Andrew G. Howard and
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Menglong Zhu and
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Andrey Zhmoginov and
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Liang{-}Chieh Chen},
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title = {Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification,
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Detection and Segmentation},
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journal = {CoRR},
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volume = {abs/1801.04381},
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year = {2018},
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url = {http://arxiv.org/abs/1801.04381},
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archivePrefix = {arXiv},
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eprint = {1801.04381},
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timestamp = {Tue, 12 Jan 2021 15:30:06 +0100},
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biburl = {https://dblp.org/rec/journals/corr/abs-1801-04381.bib},
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bibsource = {dblp computer science bibliography, https://dblp.org}
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}
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```
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<!--
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Type: model-index
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Collections:
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- Name: MobileNet V2
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Paper:
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Title: 'MobileNetV2: Inverted Residuals and Linear Bottlenecks'
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URL: https://paperswithcode.com/paper/mobilenetv2-inverted-residuals-and-linear
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Models:
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- Name: mobilenetv2_100
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In Collection: MobileNet V2
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Metadata:
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FLOPs: 401920448
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Parameters: 3500000
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File Size: 14202571
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Architecture:
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- 1x1 Convolution
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- Batch Normalization
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- Convolution
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- Depthwise Separable Convolution
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- Dropout
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- Inverted Residual Block
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- Max Pooling
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- ReLU6
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- Residual Connection
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- Softmax
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Tasks:
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- Image Classification
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Training Techniques:
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- RMSProp
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- Weight Decay
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Training Data:
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- ImageNet
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Training Resources: 16x GPUs
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ID: mobilenetv2_100
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LR: 0.045
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Crop Pct: '0.875'
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Momentum: 0.9
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Batch Size: 1536
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Image Size: '224'
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Weight Decay: 4.0e-05
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Interpolation: bicubic
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RMSProp Decay: 0.9
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Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L955
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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv2_100_ra-b33bc2c4.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 72.95%
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Top 5 Accuracy: 91.0%
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- Name: mobilenetv2_110d
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In Collection: MobileNet V2
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Metadata:
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FLOPs: 573958832
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Parameters: 4520000
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File Size: 18316431
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Architecture:
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- 1x1 Convolution
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- Batch Normalization
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- Convolution
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- Depthwise Separable Convolution
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- Dropout
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- Inverted Residual Block
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- Max Pooling
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- ReLU6
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- Residual Connection
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- Softmax
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Tasks:
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- Image Classification
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Training Techniques:
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- RMSProp
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- Weight Decay
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Training Data:
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- ImageNet
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Training Resources: 16x GPUs
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ID: mobilenetv2_110d
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LR: 0.045
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Crop Pct: '0.875'
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Momentum: 0.9
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Batch Size: 1536
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Image Size: '224'
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Weight Decay: 4.0e-05
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Interpolation: bicubic
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RMSProp Decay: 0.9
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Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L969
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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv2_110d_ra-77090ade.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 75.05%
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Top 5 Accuracy: 92.19%
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- Name: mobilenetv2_120d
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In Collection: MobileNet V2
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Metadata:
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FLOPs: 888510048
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Parameters: 5830000
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File Size: 23651121
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Architecture:
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- 1x1 Convolution
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- Batch Normalization
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- Convolution
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- Depthwise Separable Convolution
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- Dropout
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- Inverted Residual Block
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- Max Pooling
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- ReLU6
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- Residual Connection
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- Softmax
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Tasks:
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- Image Classification
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Training Techniques:
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- RMSProp
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- Weight Decay
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Training Data:
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- ImageNet
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Training Resources: 16x GPUs
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ID: mobilenetv2_120d
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LR: 0.045
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Crop Pct: '0.875'
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Momentum: 0.9
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Batch Size: 1536
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Image Size: '224'
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Weight Decay: 4.0e-05
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Interpolation: bicubic
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RMSProp Decay: 0.9
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Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L977
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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv2_120d_ra-5987e2ed.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 77.28%
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Top 5 Accuracy: 93.51%
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- Name: mobilenetv2_140
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In Collection: MobileNet V2
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Metadata:
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FLOPs: 770196784
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Parameters: 6110000
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File Size: 24673555
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Architecture:
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- 1x1 Convolution
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- Batch Normalization
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- Convolution
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- Depthwise Separable Convolution
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- Dropout
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- Inverted Residual Block
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- Max Pooling
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- ReLU6
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- Residual Connection
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- Softmax
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Tasks:
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- Image Classification
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Training Techniques:
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- RMSProp
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- Weight Decay
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Training Data:
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- ImageNet
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Training Resources: 16x GPUs
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ID: mobilenetv2_140
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LR: 0.045
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Crop Pct: '0.875'
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Momentum: 0.9
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Batch Size: 1536
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Image Size: '224'
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Weight Decay: 4.0e-05
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Interpolation: bicubic
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RMSProp Decay: 0.9
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Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L962
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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv2_140_ra-21a4e913.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 76.51%
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Top 5 Accuracy: 93.0%
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--> |