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transformers/docs/source/en/model_doc/mobilenet_v2.md
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transformers/docs/source/en/model_doc/mobilenet_v2.md
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<!--Copyright 2022 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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rendered properly in your Markdown viewer.
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-->
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*This model was released on 2018-01-13 and added to Hugging Face Transformers on 2022-11-14.*
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<div style="float: right;">
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<div class="flex flex-wrap space-x-1">
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<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-EE4C2C?style=flat&logo=pytorch&logoColor=white">
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</div>
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</div>
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# MobileNet V2
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[MobileNet V2](https://huggingface.co/papers/1801.04381) improves performance on mobile devices with a more efficient architecture. It uses inverted residual blocks and linear bottlenecks to start with a smaller representation of the data, expands it for processing, and shrinks it again to reduce the number of computations. The model also removes non-linearities to maintain accuracy despite its simplified design. Like [MobileNet V1](./mobilenet_v1), it uses depthwise separable convolutions for efficiency.
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You can all the original MobileNet checkpoints under the [Google](https://huggingface.co/google?search_models=mobilenet) organization.
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> [!TIP]
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> Click on the MobileNet V2 models in the right sidebar for more examples of how to apply MobileNet to different vision tasks.
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The examples below demonstrate how to classify an image with [`Pipeline`] or the [`AutoModel`] class.
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<hfoptions id="usage-img-class">
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<hfoption id="Pipeline">
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```python
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import torch
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from transformers import pipeline
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pipeline = pipeline(
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task="image-classification",
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model="google/mobilenet_v2_1.4_224",
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dtype=torch.float16,
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device=0
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)
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pipeline("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg")
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```
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</hfoption>
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<hfoption id="AutoModel">
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```python
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import torch
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import requests
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from PIL import Image
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from transformers import AutoModelForImageClassification, AutoImageProcessor
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image_processor = AutoImageProcessor.from_pretrained(
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"google/mobilenet_v2_1.4_224",
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)
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model = AutoModelForImageClassification.from_pretrained(
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"google/mobilenet_v2_1.4_224",
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)
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url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
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image = Image.open(requests.get(url, stream=True).raw)
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inputs = image_processor(image, return_tensors="pt")
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with torch.no_grad():
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logits = model(**inputs).logits
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predicted_class_id = logits.argmax(dim=-1).item()
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class_labels = model.config.id2label
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predicted_class_label = class_labels[predicted_class_id]
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print(f"The predicted class label is: {predicted_class_label}")
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```
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</hfoption>
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</hfoptions>
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## Notes
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- Classification checkpoint names follow the pattern `mobilenet_v2_{depth_multiplier}_{resolution}`, like `mobilenet_v2_1.4_224`. `1.4` is the depth multiplier and `224` is the image resolution. Segmentation checkpoint names follow the pattern `deeplabv3_mobilenet_v2_{depth_multiplier}_{resolution}`.
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- While trained on images of a specific sizes, the model architecture works with images of different sizes (minimum 32x32). The [`MobileNetV2ImageProcessor`] handles the necessary preprocessing.
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- MobileNet is pretrained on [ImageNet-1k](https://huggingface.co/datasets/imagenet-1k), a dataset with 1000 classes. However, the model actually predicts 1001 classes. The additional class is an extra "background" class (index 0).
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- The segmentation models use a [DeepLabV3+](https://huggingface.co/papers/1802.02611) head which is often pretrained on datasets like [PASCAL VOC](https://huggingface.co/datasets/merve/pascal-voc).
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- The original TensorFlow checkpoints determines the padding amount at inference because it depends on the input image size. To use the native PyTorch padding behavior, set `tf_padding=False` in [`MobileNetV2Config`].
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```python
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from transformers import MobileNetV2Config
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config = MobileNetV2Config.from_pretrained("google/mobilenet_v2_1.4_224", tf_padding=True)
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```
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- The Transformers implementation does not support the following features.
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- Uses global average pooling instead of the optional 7x7 average pooling with stride 2. For larger inputs, this gives a pooled output that is larger than a 1x1 pixel.
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- `output_hidden_states=True` returns *all* intermediate hidden states. It is not possible to extract the output from specific layers for other downstream purposes.
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- Does not include the quantized models from the original checkpoints because they include "FakeQuantization" operations to unquantize the weights.
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- For segmentation models, the final convolution layer of the backbone is computed even though the DeepLabV3+ head doesn't use it.
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## MobileNetV2Config
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[[autodoc]] MobileNetV2Config
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## MobileNetV2FeatureExtractor
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[[autodoc]] MobileNetV2FeatureExtractor
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- preprocess
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- post_process_semantic_segmentation
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## MobileNetV2ImageProcessor
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[[autodoc]] MobileNetV2ImageProcessor
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- preprocess
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- post_process_semantic_segmentation
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## MobileNetV2ImageProcessorFast
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[[autodoc]] MobileNetV2ImageProcessorFast
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- preprocess
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- post_process_semantic_segmentation
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## MobileNetV2Model
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[[autodoc]] MobileNetV2Model
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- forward
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## MobileNetV2ForImageClassification
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[[autodoc]] MobileNetV2ForImageClassification
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- forward
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## MobileNetV2ForSemanticSegmentation
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[[autodoc]] MobileNetV2ForSemanticSegmentation
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- forward
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