Mobilenet V2 1.4 224

🚀 MobileNet V2

A pre - trained MobileNet V2 model on ImageNet - 1k at a resolution of 224x224, offering efficient image classification capabilities.

🚀 Quick Start

You can use the raw model for image classification. Check out the model hub to find fine - tuned versions for tasks that interest you.

✨ Features

• Resource - efficient: MobileNets are small, low - latency, and low - power models, suitable for a variety of use cases with resource constraints.
• Versatile applications: Can be used for classification, detection, embeddings, and segmentation, similar to other large - scale models.
• Trade - off flexibility: Allows for a trade - off between latency, size, and accuracy.

📦 Installation

No specific installation steps are provided in the original document.

💻 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 MobileNetV2FeatureExtractor, MobileNetV2ForImageClassification
from PIL import Image
import requests

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

feature_extractor = MobileNetV2FeatureExtractor.from_pretrained("Matthijs/mobilenet_v2_1.0_224")
model = MobileNetV2ForImageClassification.from_pretrained("Matthijs/mobilenet_v2_1.0_224")

inputs = feature_extractor(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_1.0_224, where 1.0 is the depth multiplier and 224 is the resolution of the input images the model was trained on.

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