Mobilenet V1 0.75 192

🚀 MobileNet V1

The MobileNet V1 model is pre - trained on ImageNet - 1k at a resolution of 192x192. It offers efficient image classification capabilities, balancing latency, size, and accuracy.

🚀 Quick Start

The MobileNet V1 model is pre - trained on ImageNet - 1k at a resolution of 192x192. It was introduced in MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications by Howard et al, and first released in this repository.

Disclaimer: The team releasing MobileNet V1 did not write a model card for this model so this model card has been written by the Hugging Face team.

✨ Features

• Small 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 other popular large - scale models.
• Good Trade - off: MobileNets trade off between latency, size, and accuracy while comparing favorably with popular models from the literature.

📚 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.

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.

How to use

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_v1_0.75_192")
model = AutoModelForImageClassification.from_pretrained("google/mobilenet_v1_0.75_192")

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])

⚠️ Important Note

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

💡 Usage Tip

Currently, both the feature extractor and model support PyTorch.

📄 License

other