Mobilenet V2 0.35 96

🚀 MobileNet V2

The MobileNet V2 model is pre-trained on ImageNet-1k at a resolution of 96x96. It offers efficient image classification capabilities with low latency and power consumption.

🚀 Quick Start

You can use the raw model for image classification. See the model hub to look for fine-tuned versions on a task that interests you.

✨ Features

• Lightweight and Efficient: MobileNets are small, low-latency, low-power models parameterized to meet the resource constraints of a variety of use cases.
• Versatile Applications: They can be built upon for classification, detection, embeddings and segmentation similar to how other popular large scale models, such as Inception, are used.
• Trade-off Optimization: MobileNets trade off between latency, size and accuracy while comparing favorably with popular models from the literature.

📦 Installation

No specific installation steps are provided in the original README.

💻 Usage Examples

Basic Usage

Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:

from transformers import AutoImageProcessor, AutoModelForImageClassification
from PIL import Image
import requests

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)

preprocessor = AutoImageProcessor.from_pretrained("google/mobilenet_v2_0.35_96")
model = AutoModelForImageClassification.from_pretrained("google/mobilenet_v2_0.35_96")

inputs = preprocessor(images=image, return_tensors="pt")

outputs = model(**inputs)
logits = outputs.logits

# model predicts one of the 1000 ImageNet classes
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])

Note: This model actually predicts 1001 classes, the 1000 classes from ImageNet plus an extra “background” class (index 0).

Currently, both the feature extractor and model support PyTorch.

📚 Documentation

Model description

From the original README:

MobileNets are small, low-latency, low-power models parameterized to meet the resource constraints of a variety of use cases. They can be built upon for classification, detection, embeddings and segmentation similar to how other popular large scale models, such as Inception, are used. MobileNets can be run efficiently on mobile devices [...] MobileNets trade off between latency, size and accuracy while comparing favorably with popular models from the literature.

The checkpoints are named mobilenet_v2_depth_size, for example mobilenet_v2_0.35_96, where 0.35 is the depth multiplier and 96 is the resolution of the input images the model was trained on.

Intended uses & limitations

You can use the raw model for image classification. See the model hub to look for fine-tuned versions on a task that interests you.

BibTeX entry and citation info

@inproceedings{mobilenetv22018,
  title={MobileNetV2: Inverted Residuals and Linear Bottlenecks},
  author={Mark Sandler and Andrew Howard and Menglong Zhu and Andrey Zhmoginov and Liang-Chieh Chen},
  booktitle={CVPR},
  year={2018}
}

📄 License

License: other

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