Efficientnet B5

🚀 EfficientNet (b5 model)

The EfficientNet model is trained on ImageNet-1k at a resolution of 456x456, offering a powerful solution for image classification tasks.

🚀 Quick Start

The EfficientNet model was trained on the ImageNet-1k dataset at a resolution of 456x456. It was introduced in the paper EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks by Mingxing Tan and Quoc V. Le, and first released in this repository.

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

✨ Features

• EfficientNet is a mobile-friendly pure convolutional model (ConvNet).
• It proposes a new scaling method that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient.

📚 Documentation

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:

import torch
from datasets import load_dataset
from transformers import EfficientNetImageProcessor, EfficientNetForImageClassification

dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]

preprocessor = EfficientNetImageProcessor.from_pretrained("google/efficientnet-b5")
model = EfficientNetForImageClassification.from_pretrained("google/efficientnet-b5")

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

with torch.no_grad():
    logits = model(**inputs).logits

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

For more code examples, we refer to the documentation.

BibTeX entry and citation info

@article{Tan2019EfficientNetRM,
  title={EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks},
  author={Mingxing Tan and Quoc V. Le},
  journal={ArXiv},
  year={2019},
  volume={abs/1905.11946}
}

📄 License

This project is licensed under the Apache-2.0 license.